Accelerated drug development using a digital formulator and a self-driving tableting data factory

Domain: biology
Published: 2026-04-01
Editorial outcome: major_revision

Abstract
Advances in drug discovery and clinical research have shifted the bottleneck in medicines
development to chemistry, manufacturing, and controls activities, a critically step for regulatory
approval. This includes formulation and process development of a new drug product, which
traditionally requires extensive resources, often leading to suboptimal outcomes. These
development processes must adapt to follow the advances in drug discovery and clinical research
and ultimately shorten timelines while ensuring product quality and safety. In this work, we present
an integrated platform for tablet formulation and process development that couples a digital
formulator, an in-silico optimisation tool using a predictive material-to-tablet model, with a self-
driving tableting data factory, which applies Bayesian optimisation within an automated, fully
integrated per-tablet manufacturing to testing workflow. The results demonstrate a reduction in the
time from material characterisation to in-specification tablets to 6 h and a reduction in API material
use by 65% compared to current state-of-the-art methods.

Introduction
A wave of artificial intelligence (AI) enabled technologies1,2 for drug discovery and clinical trials
are transforming the way new medicines are discovered and evaluated for efficacy and safety. AI-
native drug discovery companies generated an average annual drug pipeline growth rate of around
36% from 2010 to 20213, showing an exponential rise of candidates coming to clinical trials. Most
global pharmaceutical companies partner with AI technology providers to accelerate drug
discovery and clinical research to exploit emerging digital technologies in drug target
identification4,5, generative molecular design6, automating discovery7, clinical study protocol
optimisation8, selection of optimal subpopulations9, dose optimisation10, therapeutic drug
monitoring and dynamic personalised therapy11, and reducing adverse drug reaction12. It is
estimated that these scientific advances can shorten drug development timelines from ≈ 12-15
years to 3-4 years13. Advances in drug discovery and clinical research have shifted the primary
bottleneck in efficiently delivering new medicines to patients. In particular, chemistry,
manufacturing, and controls (CMC) activities, crucial for regulatory approval, now represent a key
step on the critical path to registering new medicines. CMC includes the development of
manufacturing routes and processes, formulation, scale-up and a robust control strategy that
reliably provides quality products to patients. This development process involves numerous
dependent decisions to transform a new drug candidate into a final product that can be
manufactured at scale and meets the target product profile (TPP). From a drug product for first in
human (FIH) clinical trials to commercialisation, the financial and time penalties of changing a
decision rise exponentially over the course of development using current procedures14. CMC
development processes must therefore adapt to follow the advances in drug discovery and clinical
research and ultimately shorten timelines while ensuring product quality and safety. Digitalisation
of CMC processes by the utilisation of digital tools such as big data generation, predictive
modelling and AI, automation and robotics can help accelerate drug development timelines by
reducing the experimental burden and increasing lab efficiency.

Roughly two-thirds of medicines are administered orally, and approximately half of these
medications are in the form of a tablet15. Small molecule drugs continue to represent the majority
of the pipeline with 52% drugs (29 out of 55) approved by the U.S. food and drug administration
(FDA) in 2023 classified as small molecule16, in line with the five-year average for this modality17.
Most small molecule active pharmaceutical ingredients (APIs) coming through the pipeline lack
desirable raw material characteristics such as good flowability, compressibility, compactability
and solubility that are necessary for ease of manufacture and meeting the TPP targets18,19. This
puts additional pressure on making informed decisions on formulation, manufacturing route and
processes.

Process and formulation design follows a cycle of hypothesis generation, experimental design, lab
testing, and data interpretation to understand effects of various formulation and process factors
and design a consistent high-quality product. Yet, this process is often inefficient, repetitive, and
time-consuming, potentially spanning months to years. Recent advancements in predictive
modelling have been applied across CMC activities, with a particular focus on direct compression
(DC) of pharmaceutical tablets, due to its simplicity, cost-effectiveness, and suitability for heat-
and moisture-sensitive APIs20. DC eliminates the need for granulation steps, thereby reducing
production time and preserving the integrity of sensitive compounds21. However, formulators often
consider granulation due to DC’s high dependency on the material properties of the API and
excipients (e.g., flowability, compressibility, and compactability). This challenge can be addressed
through an autonomous, resource-efficient approach using industrial digital technologies aligned
with the manufacturing classification system (MCS) 18,22,23 to proactively scope the likelihood of
using DC as a viable processing option for a given formulation to meet the TPP.

Mechanistic models - including first-principle and empirical 24-27 - have been utilised to predict
tablet formulation and processing parameters. While these models provide valuable insights into
the underlying physical and chemical processes, they require extensive experimental data and in-
depth physics-based (mechanistic) knowledge of the problem to estimate their parameters
accurately. This demands significant effort in preparing and calibrating formulations and tablets
with often partial domain knowledge. Data-driven models, leveraging machine learning28-31, deep
learning, and computer vision32-35, offer alternative approaches by identifying complex patterns
within experimental data. However, the data processing and training/testing pipelines often lack
generalisability. This highlights the need for a hybrid approach that integrates mechanistic
understanding with data-driven techniques, minimising experimental burdens while predicting the
drug product properties directly from raw material attributes36.

Another critical aspect in the development process is the systematic optimisation of decision
parameters, including formulation compositions and process configurations, to achieve the desired
TPPs. This requires researchers to navigate the complex interplay of variables, making informed
decisions that balance quality attributes, regulatory requirements, and manufacturing
efficiencies37,38. Bayesian optimisation methods powered by Gaussian processes have been tested
in pharmaceutical process engineering due to their computational efficiency39,40. However, they
are typically used for process optimisation cases with a limited number of decision parameters and
may not effectively handle larger-scale problems with numerous variables and different types of
input features41,42. Therefore, advancing modelling decision-making strategies by combining
current methodologies with more robust approaches such as gradient-based 43,44 and gradient-free
45,46 optimisation methods is crucial for addressing the correlation between different types of
decision variables in tablet formulation and manufacturing processes.

Self-driving labs have gained significant attention in recent years for their potential to revolutionise
material discovery47,48. These labs leverage robotics, automation, and AI to accelerate discovery
and development processes while reducing human error. The closed-loop workflow in a self-
driving lab is generally a dynamic operation that cycles through a design, make, test and analyse
(DMTA) workflow49-53. Relevant examples include a fully autonomous solid-state powder X-ray
diffraction workflow using multipurpose collaborative robots54, self-driving synthesis55, discovery
of heterogeneous catalysis materials 56, and an AI-Chemist platform capable of autonomously
performing chemical research tasks, including literature review, experiment design, execution
across 14 workstations, and data analysis using machine learning and Bayesian optimisation57.
Despite this rise in self-driving laboratories and predictive systems for molecular and material
discovery, the development of formulations and process conditions for oral drug products remain
a largely manual process requiring a significant time investment of subject matter experts47,58-60.
While recent advancements in self-driving laboratories have predominantly focused on liquid-
phase systems, particularly in the context of flow chemistry61 and automated optimisation of
organic reactions62, the development of autonomous platforms capable of handling solid materials
remains comparatively underexplored. Existing literature has demonstrated advanced capabilities
in optimising reaction conditions, reagent selection, and process parameters in continuous liquid
flow systems. However, translating these self-driving principles to systems involving powders and
other solid matter introduces additional complexities such as precise dosing, transport, compaction,
and real-time characterisation of particulate systems.

In this study, we address a key gap in drug product development by integrating a digital formulator
with a self-driving tableting data factory (Figure 1), aligned with the principles of quality by digital
design (QbDD)63, reducing the development time to 6 h and a reduction in API material use by
65% compared to current state-of-the-art method. The platform workflow begins with defining the
specifications for the new drug product for a given API (input information in Figure 1), including
a target drug loading and manufacturability criteria. It then proceeds through two integrated
components: the digital formulator and the tableting data factory. The digital formulator employs
a hybrid system of models and in-silico optimisation to select the optimal combination of
excipients (filler/binder 1 and 2), their respective mass fractions, and initial compaction pressure.
The objective is to maximise the flowability of the blend while satisfying a key manufacturability
constraint: achieving a minimum tablet tensile strength at specified porosity. Although these
constraints can be adjusted, they were set in this study to a tensile strength > 2 MPa at 15% porosity.
The tableting data factory is a physically and digitally integrated cyber-physical system for
automated dosing, transport and characterisation of powder, and tablet manufacturing and testing,
i.e., the make & test system. The orchestration system coordinates the physical and digital tasks,
including the data acquisition, processing and management across all stages of the automated make
& test system. Once the optimised formulation is established by the digital formulator, the tableting
data factory applies an experimental agent, a Bayesian optimisation approach, to refine the
compaction pressure and validate the selected formulation against the defined manufacturability
constraint.

Each component of the platform is independently validated using separate experimental protocols
and materials. The fully integrated workflow (Figure 1) is then demonstrated across nine uses cases,
involving six distinct APIs including one API evaluated at four different drug loadings.

Results
Digital formulator: hybrid system of models
The system of models (Figure 2) is comprised of two connected steps: 1) mixture models 65 and
particle informatics66, and 2) process models. The mixture models predict the true density, bulk
density, tapped density, particle size distribution, aspect ratio distribution and flow function
coefficient (FFC) of a blend of materials for a given formulation (API and excipients with their
mass fractions) from the raw material’s true density, bulk density, particle size distribution and
aspect ratio distribution. The material properties of excipients are available in the excipient library
with 32 grades of excipient materials that include various grades of microcrystalline cellulose
(MCC), mannitol, lactose, dicalcium phosphate anhydrous (DCPA), croscarmellose sodium (CCS),
and magnesium stearate (MgSt). A subset of these materials was used for the training and
validation of the hybrid system of models. The process models utilise the output of the mixture
models with additional input features about the API (i.e., the particle informatics descriptors and
API concentration) and the compaction pressure. The mixture models were trained and validated
in Salehian et al. 65, while this study provides additional validation data (𝑅2= 0.93 and RMSE=
0.013), comprising 220 data points across 44 formulations, for the FFC model (Supplementary
Figure 6).

Two ensemble sets of deep neural networks (DNNs) were trained to predict the porosity and tensile
strength of tablets from blend material properties, API concentration, and compaction pressure.
The blend material properties for both training and validation were predicted using the mixture
models with the raw material attributes as input features. Both DNNs were trained and validated
on a dataset comprised of 1199 tableting data points from 170 different formulations across 15
different APIs and nine different binders/fillers. Model training was conducted using data from
113 formulations, including placebo and four different APIs, resulting in 653 data points collected
under varying compression pressures. Model validation was performed on an independent dataset
consisting of 57 formulations across 11 different APIs, comprising 546 data point obtained under
varied compression pressures. Placebo formulations were used exclusively for model training, and
no API grade included in the training set was used in the validation set. Both DNN models showed
strong performance, achieving R2 values of 0.90 for porosity and 0.89 for tensile strength. The
ensemble learning strategy used to train the DNNs allows the prediction of the uncertainty, i.e.,
standard deviation of predicted values over the ensemble of models. While higher standard
deviations of porosity predictions are associated with higher porosity values (i.e., regions with
sparse training data67), the trend for uncertainty predictions of the tensile strength is less consistent
(Figure 3). This discrepancy can be attributed to the inherently more complex and nonlinear
relationship between input features and tensile strength68,69. Tensile strength is more susceptible
to both aleatory uncertainty (e.g., inherent variability in material behaviour) and epistemic
uncertainty (e.g., limited knowledge), which can compound mixed behaviour in the estimation of
uncertainty even when using robust ensemble methods70.

Critically, the modelling framework is designed to be material-agnostic, using exclusively
fundamental material characteristics (bulk density, true density, particle size and shape
distributions) rather than material identity, enabling generalisation to new materials provided their
properties fall within the property space used to train the models (Supplementary Figs. 1-5).

Digital formulator: in-silico optimisation
The digital formulator uses the hybrid system of models in an optimisation framework (Figure 2)
to find the optimal set of filler/binder 1 and 2, their respective mass fractions, and the initial main
compression pressure for a given API and target drug loading, and given disintegrant and lubricant
material choices and mass fractions. The objective is to maximise the FFC subject to a
manufacturability constraint, i.e., satisfy a minimum tablet tensile strength at a given porosity.
The modular architecture of the digital formulator allows subject matter experts to define exclusion
criteria for excipients (set by the user in the excipient sub-selection) based on prior knowledge.
For example, incompatible API-excipient and excipient-excipient combinations excluded using
their simplified molecular input line entry system (SMILES)71 representations with compatibility
models72,64 or through experimental screening results73,74. This expert-guided approach ensures
that only compatible material combinations proceed through the optimisation workflow, reducing
the likelihood of selecting a formulation that may exhibit physical or chemical instabilities75,76.

Tableting data factory
Following the output from the digital formulator, the platform workflow advances to the tableting
data factory (Figure 1). The tableting data factory comprises of three key components: make &
test, an orchestration system and an experimental agent.

Tableting data factory: make & test
This setup integrates commercially available devices with several customised features (Figure 4b).
Robotic arm 1 (R1) is employed for material transport and to physically interconnect multiple
devices. Each iteration of the tablet production begins with the autonomous acquisition of a near-
infrared (NIR) spectroscopic dark scan and a reference scan, using a 99% reflectance disc
permanently attached to the R1 gripper. The orchestration system captures the reference scan once
R1 positions the disc on the NIR spectrometer. Subsequently, R1 proceeds to the dosing unit to
acquire the dose for a single tablet. The dosing unit, preloaded with a premixed powder blend,
dispenses the precise amount of powder required for one tablet into a customised 3D-printed
transportation unit (TU) that is placed on a balance (Supplementary Note 3.1.2). The first decision
point (D1) concerns the weight of the obtained powder, measured in real-time (Figure 4a). Any
dose deviating by more than ±5% from the target is rejected and subsequently recycled. The
powder dose, that satisfies the weight thresholds, moves to the NIR spectrometer stage where the
spectrum is acquired through a sapphire window which is installed at the bottom of the TU. The
acquired spectrum is used to monitor the blend homogeneity through monitoring Hotelling’s T2
analysis (Supplementary Note 4.3.1). R1 then transports the TU to the compaction simulator,
depositing the powder into a 9 mm round-shaped die. Tablets formed in the compaction simulator
are conveyed to an automated tablet tester via a customised chute. The automated tablet tester
conducts destructive testing on selected tablets, measuring weight, thickness, diameter, and
breaking force to determine tablet porosity and tensile strength. The number of tablets undergoing
destructive testing is set in the experimental protocol by the user. The remaining tablets undergo
non-destructive testing, which measures all parameters except breaking force. A customised tablet
separator (TS) then segregates the damaged from the undamaged tablets (Supplementary Note
3.1.4).

The second decision point (D2) assesses whether tablets should be discarded if they fail to meet
quality standards such as tablet weight, porosity and tensile strength beyond acceptable variations
(± 5%). Undamaged tablets are collected by the robotic arm 2 (R2) using customised gripper
fingers and stored in designated containers. Finally, to prevent cross-contamination with other
blends, a customised 3D-printed cleaning unit (CU) is used to thoroughly clean the tube and casing
of the TU (Supplementary Note 3.1.5).

Benchmarking and validation of the system is focused on assessing 1) the powder and tablet weight,
and any related powder loss caused by the powder handling and transportation, and 2) the
consistency of repeated experiments.

The primary objective in the design of the TU was to discharge the powder precisely while keeping
the powder loss in a consistent range across different formulations. Therefore, powder loss
between the powder obtained, measured by the balance, and the final tablet weight was assessed
across ten different formulations and three different target tablet weights (200, 300, and 400 mg)
(Supplementary Note 3.1.8). The total powder loss during transportation, including losses from
powder dosing and spillage when opening the TU gate in the compaction simulator, consistently
stays within 10-22 mg across different formulations and dose weights (Supplementary Note 3.1.8).
Material is primarily lost in bulk rather than selectively. Therefore, the ratio of API to excipients
remained stable throughout the process. Moreover, the relative standard deviation in powder
obtained and the tablet weight remains below 3%. The mean deviation from target tablet weight
and the porosity for the different formulations tested was below 4% and 1%, respectively,
demonstrating an acceptable consistency across these parameters.

Tableting data factory: orchestration
The orchestration system serves as the central intelligence of the entire setup, enabling seamless
integration of all instruments, coordinates the interaction between various hardware components,
and ensures that each instrument performs its function according to a predefined experimental
workflow (Supplementary Note 3.1.7). Additionally, the orchestration system is responsible for
real-time data acquisition and logging, allowing for synchronised data flow across the process
chain. The orchestration system not only integrates and controls all instruments but also serves as
the platform where the entire experimental workflow is designed and managed. This unified
environment allows users to define the sequence of operations, set parameters for each stage, and
establish decision-making logic within a single interface.

Tableting data factory: experimental agent
The primary experimental agent used in this study was a physics-informed Bayesian optimisation
(PIBO) framework. PIBO optimises the main compression pressure to meet the target porosity
while taking the existing physics-based (PB) correlations between the input and objectives into
account, leading to faster convergence and reduced number of experiments required to generate
the compressibility and compactability profiles. The PIBO framework aims to optimise the main
compression pressure to achieve the target porosity and tensile strength while satisfying the
underlying (empirical) compressibility and compactability models that provide prior information
about the relationship between main compression pressure, porosity, and tensile strength. The
experimental agent is fully customisable and can be adapted for a range of applications; an
additional example illustrating its use for scale-up assessment is provided in Supplementary Note
3.2.3.

Demonstration of platform workflow
The platform workflow (Figure 1) was applied to nine case studies (Figure 5), comprising five
different APIs while one API was studied across four different drug loadings. For each case study,
the raw material properties of the API were measured. Across all case studies, the lubricant and
disintegrant material and their mass fractions were kept constant with CCS at 3.5 wt.% and MgSt
at 1 wt.%. We selected a subset of the excipients (Figure 5a) for the optimisation which align with
common materials to ensure findings are relevant to real-world applications77,78. It should be noted
that the hybrid system is inherently flexible and can readily accommodate an expanded list of
excipients as appropriate. The optimisation problem was set up to maximise the FFC of the blend
at consolidation pressure of 1.6 kPa with the constraint of meeting > 2 MPa of tensile strength at
a 15% porosity tablet. These constraints are based on literature recommendations18,22 to deliver a
tablet that has a sufficient strength while it is also porous enough to ensure fast liquid uptake and,
consequently, effective disintegration of the tablet as required for immediate release formulations.

For each case study, the digital formulator was run to optimise the selection and mass fraction of
filler/binder 1 and 2 as well as the initial main compression pressure (Figure 5b). In all cases
(except GR 20 wt.% where Lactose FastFlo 316 has the higher concentration), single or multiple
grades of MCC dominate the optimal solutions, demonstrating its significant role in achieving the
required tensile strength within the imposed constraints which is attributed to its superior
compactability properties78,79. Conversely, other excipients such as Lactose Granulac 200M and
Mannitol Pearlitol 200 SD are either minimally utilised or entirely absent from the optimal
formulations, indicating their limited contribution to maximising the FFC under the given
manufacturability constraint. The sporadic, concentration-dependent inclusion of Lactose FastFlo
316 and MCC Avicel PH101 suggests that their presence in the formulation is highly sensitive to
the specific API and its loading. There are, however, multiple other factors that needs to be further
considered when optimising the formulation. For example, the proportion of MCC can be
potentially restricted due its insoluble nature, which can affect the tablet's dissolution rate and API
bioavailability80. It also has a high moisture content, posing stability issues for moisture-sensitive
APIs81. Moreover, MCC is strain rate sensitive, which can lead to inconsistencies in tablet strength
during high-speed manufacturing82. Therefore, despite its advantages, it's important to balance
MCC's use by considering these factors and potentially exploring alternative excipients to address
these challenges. Another parameter is the impact of lubricant and disintegrant on the
manufacturability and performance (e.g., disintegration, dissolution) criteria, which remains a
promising topic to be studied in a future work.

The comparison between predicted and measured FFC values (Supplementary Figure 25) shows
that predicted FFC values generally trend higher than measured values, particularly for Aspirin
(AS; 20 wt.%) and Griseofulvin (GR; 20 wt.%), attributable to the model error and the uncertainty
of flowability data especially in higher FFC values. Despite being lower, the measured FFC values
of all optimal formulations were validated through the preparation and characterisation of the
respective blends with no flowability issues observed during the processing confirmed through the
assessment of powder loss and weight uniformity (Supplementary Figure 39a). Additionally, the
validation of the FFC model with an expanded dataset reveals consistent performance to its
original version (Supplementary Figure 6). This suggests that, while the predictive model may
pose prediction errors, it effectively guides the formulation process towards producing robust and
viable tablets. While there are inherent errors in predictions, the model’s prediction accuracy
remains superior as compared to the state-of-the-art using both regression83 and classification84
approaches.

The PIBO framework was tested with the tableting data factory across the nine formulations
identified by the digital formulator to achieve target porosity of 15%. During the optimisation
process, the tuning parameters of the physics-based models varied initially, then converged to a
plateau after several iterations (Figure 5c). The termination criteria (based on the change in the
goodness of fit of empirical compressibility and compactability models to the collected data) were
activated during different iterations based on the speed of convergence in each case study. The
termination of optimisation is followed by a validation experiment at the target porosity, where a
compression pressure is suggested based on the physics-based models with the identified tuning
parameters.

The measured porosity is compared with the predicted target to evaluate the accuracy of calibrated
models. The digital formulator, on average, overestimated the porosity by 0.26% and
underestimated the tensile strength by 0.63 MPa (Figure 5d). The comparison between the initial
prediction by the digital formulator and calibrated models after PIBO demonstrates that the initial
predictions (Supplementary Figs. 37 and 38), are relatively close to the final calibrated models.
While the PIBO calibration process further refines the predictions, the adjustments required for
the initially predicted profiles by the digital formulator require only 4 - 6 experiments for the case
studies investigated in this work, underscoring the reliability of the DNN-based process models in
making a close-to-optimum first-time prediction of tablet attributes from raw material properties.
The general underestimation of the initial predictions is due to the conservative definition of the
objective function to justify the optimisation in scenarios where extensive experimentation may
not be feasible, and a right-first-time formulation optimisation is required.

The assessment of blend homogeneity was performed qualitatively using Hotelling’s T2 analysis
(Supplementary Figure 36), which evaluates the multivariate distance of each spectrum from the
principal component analysis (PCA) model centre to identify any deviations between iterations.
This approach confirms the blend subsamples do not deviate significantly from one another,
supporting the assumption that the blend is homogeneous. Across all case studies, the tablets met
the manufacturability constraint (> 2 MPa at 15% porosity) and satisfied the disintegration
requirement in less than 15 min (Supplementary Figure 42), in line with expectations for
immediate release tablet formulations.

API particle size impact on manufacturability
The hybrid system of models in the digital formulator can be used to generate new understanding
of the effect of various physical properties such as particle size distribution (PSD) on blend
flowability and tablet tensile strength. To demonstrate this, we systematically examined different
types of PSDs (e.g., unimodal and bimodal) with distinct characteristics to elucidate the
relationship between PSD and blend flowability (FFC) and tablet tensile strength. Understanding
these relationships is crucial for particle engineering and formulation scientists seeking process
insights to optimise API particle characteristics and tablet properties.

We investigated 16 synthetic APIs with distinct particle size distributions: 8 unimodal (UMD) and
8 bimodal (BMD) distributions. The distributions were synthetically generated by parametrising
log-normal distributions (Supplementary Note 4.2.2), covering a range of values across the
parameter space used to train and validate the hybrid system of models. Generated distributions
were used as the API PSD to simulate blend and tablet properties using the hybrid system of
models. All other properties of the API (e.g., true and bulk densities, aspect ratio distribution,
particle informatics descriptors) were kept identical to paracetamol (SP). The formulation used for
simulation consisted of API at varying drug loadings, 3.5% of CCS, 1% of MgSt, and equal
concentrations (1:1 ratio) of Lactose FastFlo 316 (LAC1) and MCC Avicel PH102 (MCC2),
followed by a 100% drug loading as pure API. Simulations were run for each PSD at different
drug loadings, varying from 0 (i.e., placebo) to 100% (i.e., pure API) with 20% increments in drug
loading. For each case, flowability of the formulation (FFC at 1.6 kPa) as well as the porosity and
tensile strength within a range of compression pressure from 100 MPa to 400 MPa with 50 MPa
increments were predicted, followed by interpolating the tensile strength at 15% porosity through
fitting the Ryshkewitch-Duckworth model85 to the simulation data. Supplementary Tables 11 and
12 list the user-defined parameters to generate UMD and BMD, denoting the synthetically
generated unimodal and bimodal distributions, respectively. The increasing integer in UMD1 to
UMD8 and BMD1 to BMD8 reflect a progressive increasing in mean particle size and/or span.
Supplementary Tables 13 and 14, respectively, summarise the simulation outputs for flowability
and tensile strength.

The systematic investigation of particle size distribution effects on FFC (Figure 6a) shows that
finer particle distributions (e.g., UMD1-UMD2) exhibit significant reductions in FFC as drug
loading increases, decreasing from 6.5 to 1.2 and 6.6 to 1.4 respectively, crossing into the very
cohesive flow regime (FFC < 2) 86,87. The exceptional flow improvement noted for UMD7, BMD7,
and BMD8 can be attributed to two key factors: (1) reduction in specific surface area, minimising
cohesive interactions88, (2) improved packing efficiency through enhanced void structures that
improve powder mobility89,90. Accordingly, UMD7’s exceptional improvement in FFC can be
attributed to a lower percentage of fine particles (as detailed in Supplementary Table 11, showing
the percentage of particles below a specified size threshold), despite UMD8 possessing a larger
median particle size (𝐷 ). These mechanisms are also supported by studies of granular flow in
50
pharmaceutical powder systems, where the presence of larger particles reduces interparticle
friction and facilitates improved flow91,92.

Figure 6b shows the results for tensile strength at 15% porosity using unimodal and bimodal API
PSDs at different drug loadings. For all particle size distributions, tensile strength showed a
pronounced decline as drug loading increased. This trend aligns with established understanding
that higher API concentrations typically reduce tablet strength due to the poorer compactability of
crystalline APIs93,94. A marked decline occurs when drug loadings exceed 20% providing model-
based confirmation of the established empirical understanding that percolation thresholds are
typically low for many API materials in direct compression processes18. This computational
validation of the ≈20% threshold aligns with industry experience that higher drug loadings create
formulation challenges due to the formation of percolating networks that compromise tablet
mechanical properties95,96.

At drug loadings up to 60%, bimodal API PSDs consistently outperformed unimodal distributions,
yielding slightly higher tensile strength at 15% porosity values, an advantage that could be
attributed to the improved packing efficiency and interparticle bonding facilitated by the presence
of both fine and coarse API particles97,98. However, at very high drug loadings (80% - 100%), this
advantage diminished and eventually reversed, with unimodal API PSDs surpassing bimodal
systems in tensile strength.

The impact of distribution type is significantly smaller than that of the drug loading on the tensile
strength. However, some trends can still be observed across both distribution types. Smaller
particle sizes generally resulted in higher tensile strength at 15% porosity at all drug loading levels.
For unimodal distributions, formulations with 𝐷 of 10 μm (e.g., UMD1, UMD2 in
50
Supplementary Table 11) maintained higher tensile strength than those with 𝐷 of 150 μm (e.g.,
50
UMD8 in Supplementary Table 11). This effect can be attributed to the increased surface area
available for interparticle bonding with smaller particles, leading to stronger compacts99. Similarly,
narrower particle size distributions (i.e., lower span values) generally resulted in tablets with
higher tensile strength compared to wider distributions at the same 𝐷 . For example, UMD1
50
showed higher tensile strength than UMD2 at all drug loading levels. This can be attributed to
more efficient particle packing and greater interparticle contact area in narrower distributions100,101.

Sensitivity analysis of optimised formulations
A sensitivity analysis was conducted on the excipient ratios of the optimised formulations
generated by the digital formulator. For each blend, the model ensemble predicted tensile strength
at a fixed porosity of 15% and the FFC (Supplementary Figs. 26–34).

Across lactose-containing formulations, higher lactose fractions consistently reduced tensile
strength at 15% porosity while increasing FFC, showing the intrinsic trade-off between the
optimisation objective (maximising blend flowability) and the constraints on tensile strength and
porosity for mechanical integrity29. The analysis also provides quantitative justification for the
selected optima. For instance, optimal formulation in SP 16% case satisfies the tensile-strength
criterion up to ≈30 wt.% lactose; beyond this threshold, tensile strength declines and its predictive
uncertainty rises sharply, whereas FFC reaches a plateau at ≈45 wt.% lactose. Similar trends are
observed for SP 18%, SP 20%, and SP 22%, where FFC plateaus at high lactose levels, but tensile
strength falls below the acceptance limit once FFC increases abruptly. In AS 20% and IM 20%,
the optimiser could propose higher lactose loadings to further improve FFC; however, the
corresponding drop in tensile strength suggests that these solutions would violate the tensile
strength constraint. Model-based uncertainty behaves as expected: predictive variance for FFC
grows with increasing FFC values, consistent with the behaviour reported for the initial model
version65. Uncertainty in tensile strength predictions is lower at moderate strengths but becomes
heterogeneous at the extremes, reflecting the complex, nonlinear coupling among excipient
properties.

Feature importance analysis
Mean SHAP values102 were calculated for both porosity and tensile strength models to enhance
interpretability and understand the relative importance of input features (Supplementary Figs. 13
and 14). Compression pressure, representing process settings, exhibited the greatest impact on both
predicted outputs, while inclusion of particle informatics descriptors showed minimal impact on
models’ prediction. Tablet porosity demonstrated the second-highest influence on tensile strength
prediction, confirming alignment with established empirical compactability models85. Tablet
porosity demonstrated the second-highest influence on tensile strength prediction, confirming
alignment with established empirical compactability models85. True density and PSD principal
component 1 (PC1), representing inherent mixture properties, showed considerable impact on both
outcomes, highlighting the importance of assessing these parameters' effects on tablet
manufacturability and quality through computational modelling.

Tableting data factory for manufacturing
The tableting data factory can be operated in a manufacturing mode, delivering small-scale batch
product for a specific set of specifications. While the current setup is not designed for good
manufacturing practice (GMP) compliance, it serves as a proof-of-concept for applications such
as early-phase clinical trials, dose titration studies, or personalised healthcare, where flexibility
and adaptability are critical103-105. Real-time process monitoring is facilitated by augmented and
mixed reality (AR/MR) visualisation tools (Supplementary Movies 2 and 3 and more details in the
Supplementary Note 5), enabling parameter monitoring and adjustment to ensure high-quality
output in real-time scenarios106. Real-time process monitoring is facilitated by augmented and
mixed reality (AR/MR) visualisation tools, enabling parameter monitoring and adjustment to
ensure high-quality output in real-time scenarios106. Real-time process monitoring is facilitated by
augmented and mixed reality (AR/MR) visualisation tools, enabling parameter monitoring and
adjustment to ensure high-quality output in real-time scenarios106.

Benchmarking and performance metrics
It is important to note that: 1) no existing model in the literature predicts tablet properties directly
from raw material attributes without requiring prior blend or tablet preparation for calibration, and
2) no fully automated experimental workflow currently exists at this scale. As a result, our
comparison is limited to specific cases where a meaningful benchmark could be established.
Specific performance metrics of the platform including the digital formulator and the tableting
data factory are107 with a comparison to state of the art where applicable:

Throughput and productivity: The data factory achieved a tablet production rate of 72 tablets per
hour, encompassing powder quality monitoring, manufacturing, testing, storage, and data handling.
A continuous run over 24 h would deliver ≈ 1,440 tablets accounting for periodic interruptions
such as cleaning cycles, hopper refilling, or brief maintenance, thus averaging 60 tablets per hour.
State-of-the-art: in drug product development, compaction simulator are most commonly used
with either manual feeding (≈ 10 tablets per hour) or a shoe / paddle feeder (≈ 20 tablets per
hour)108,109.

Material usage: A comparison with state of the art approaches demonstrates a 65% reduction in
API material usage with our platform compared to the approaches reported in the literature
(Supplementary Note 6). The platform workflow requires less than 2.65 g of API for the raw
material characterisation and the tableting data factory using the PIBO framework to make tablets
with a weight of 250 mg. The absolute amount of API material usage depends on several factors,
including the tablet weight, number of experimental repeats, and drug loading. It should be noted
that the majority of material usage in this platform arises from raw material characterisation, which
is performed only once and can subsequently be used to predict blend and tablet attributes for any
formulation State-of-the-art: The material usage of the best state-of-the-art approach, as detailed
in the Supplementary Note 6, is ≈ 7.5 g.110 This approach requires calibration and hence would
need to be repeated for blends with a different excipient.

Human resource requirements: The automated workflow requires a total of 6 h distributed across
material characterisation (4 h), formulation optimisation via the digital formulator (0.5 h), blend
preparation (1 h), and tableting using the data factory with PIBO (0.5 h). State-of-the-art: Human
resource requirements are greater, with a total of around 14 h needed for material and API
characterisation108,26, manual data handling, predictive tool use111, blend preparation, and
compaction pressure optimisation. These time estimates do not include the blend characterisation,
e.g., flowability. It should be noted that these estimates are based on industry experience due to
the lack of reported data in the literature.

Degree of autonomy: The automated system operated autonomously during the formulation
optimisation and tableting process, requiring human input only for blend preparation and hopper
filling. State-of-the-art: At present, no autonomous platform exists that addresses this specific
application.

Operational lifetime: The system maintained continuous operation with refill of the dosing hopper
required after every 100 tablets to avoid powder accumulation and ensure dosing accuracy.
Additionally, cleaning of the compaction punch was necessary after ≈ 1000 tablets. State-of-the-
art: Existing systems do not support continuous operation across all stages.

Accessible parameter space: The data factory explored a broad design space including dose weight,
pre- and main compaction pressures, dwell time, compaction profiles, and multiple methods for
NIR and tablet testing. State-of-the-art: Existing systems can access the same parameter space
using manual material and data handling.

Methods
Materials
This study used 26 different materials, including 18 different API grades and six fillers/binders,
one disintegrant and one lubricant. All materials were used as received. Details regarding the
material IDs, characteristics of the excipients and APIs can be found in Supplementary Tables 1
and 2.

Digital formulator: hybrid system of models
PCA was performed to reduce the dimensionality of the predicted particle size and aspect ratio
distributions by mixture models into two sets of three principal components112. The API
descriptors include its concentration to explicitly model the effect of drug loading on tablet
attributes, and the crystallographic and particle informatics descriptors represent properties
relevant to the processing and mechanical behaviour of the API (Supplementary Table 4)113. Table
1 summarises the input features used to train process models. For the tensile strength model, the
response variable was log-transformed (i.e., the natural logarithm of tensile strength, ln(𝜎 )) to
t
incorporate knowledge on the exponential relationship between compression pressure and tensile
strength 108.

To evaluate the models' ability to generalise to new APIs, a leave-API-out approach was employed
in splitting the dataset into training and test sets. Specifically, the validation data was composed
by 57 different formulations (amounting to 546 data points) containing Paracetamol (SP),
Griseofulvin (GR), Aspirin (AS), L-Ascorbic Acid (ASC), Ciprofloxacin Hydrochloride (CIPH),
Doxycycline Hyclate (DOX), Guaifenesin (GF), Indomethacin (IM), Levetiracetam (LVCT),
Metformin Hydrochloride (MH), Dexamethasone (DEX), three Ibuprofen grades (IBU2, IBU3,
IBU4). The training data contained 113 formulations (amounting to 653 data points) including
placebo tablets and tablets with Benzoic Acid (BZ), Lovastatin (LOV), one ibuprofen grade
(IBU1), and Mefenamic Acid (MF). It is worth noting that while IBU1 was included in the training
dataset, three additional ibuprofen grades (IBU2-4) with distinct physical properties were retained
in the validation dataset to assess the model's generalisability, aligning with the material-agnostic
nature of the framework, which relies on fundamental material properties rather than material
identity. This strategy was designed to assess the models' performance in predicting tablet
attributes for APIs not seen during training. Supplementary Table 3 summarises the tablet data
based on the API, their usage in training or validation, and range of drug loading, porosity, tensile
strength, and compression pressure.

Following the ensemble learning methodology outlined in Salehian et al. 67, an ensemble of DNNs
(with 20 models per ensemble in this study) was trained in different random seeds, with the final
output for porosity or tensile strength being the average of the individual models' outputs. This
ensemble modelling strategy enhances the robustness of the DNN by mitigating the effects of
random initialisation on training performance and enabling the estimation of the standard deviation
for future predictions – an important measure for assessing the model's prediction quality for new
data points. All DNNs were trained using the same dataset and model architecture, which consisted
of two hidden layers with 128 units, each followed by a ReLU activation function.

To mitigate the risk of overfitting, several safeguards were implemented: 1) the ensemble learning
approach reduces overfitting by averaging out model-specific biases and provides uncertainty
estimates through the standard deviation of predictions114,115. 2) A leave-API-out validation
strategy was used to evaluate generalisation performance to previously unseen APIs, which
provides a more stringent test of model robustness than conventional random splitting. 3) The
DNN architecture was kept simple with only two hidden layers of 128 units each, following ReLU
activation functions, to limit model complexity relative to the available data and balances model
expressiveness with the risk of overfitting116,117. 4) Early stopping callbacks were utilised to
prevent overfitting and optimise training duration (Supplementary Figs. 11 and 12)118. The
convergence of training and validation losses for both DNN-based porosity and tensile strength
models confirmed successful regularisation without overfitting119. Additionally, the superior
performance of DNNs over Random Forest and Support Vector Regression in the leave-API-out
validation (Supplementary Table 5) suggests that the ensemble DNN approach successfully
captures generalisable patterns rather than memorising training-specific features.

Digital formulator: in-silico optimisation
The in-silico optimisation utilising the hybrid system of models is defined as:
𝐽 (𝑥,𝑚) = −FFC
𝑥∈ℝ𝑁𝑥
𝑚∈ℝ𝑁𝑚
Subject to: (1)
𝜃 −[𝐸(𝜎̂)−𝛼 ×𝛿 ] < 0
𝜎̂ 𝜎̂
𝜃 −[𝐸(𝜖̂)−𝛽×𝛿 ] < 0
𝜖̂ 𝜖̂
where 𝑥 is the 𝑁 dimensional vector of decision variables; 𝑚 is the 𝑁 dimensional state vector
𝑥 𝑥
of raw component properties (e.g., particle size and aspect ratio distribution, true density, bulk
density); 𝐸(𝜖̂) and 𝐸(𝜎̂) are the expected mean value of the predicted porosity and tensile strength,
respectively; 𝜃 is the user-defined threshold for manufacturability conditions (𝜃 = 2 MPa and
𝜎̂
𝜃 = 0.15 in this study), 𝛿 and 𝛿 are the standard deviation of the predicted porosity and tensile
𝜖̂ 𝜖̂ 𝜎̂
strength, respectively; 𝛼 and 𝛽 are user-defined constants (both are set to 0.2 in this study) to
define the allowable level of risk in the robust optimisation process. Higher values of these risk
factors result in a more conservative optimisation, thereby reducing the likelihood that the
formulation will fail validation. Non-dominated sorting genetic algorithm II (NSGA-II)120 was
used as the optimisation algorithm due to its proven capability in global search and independence
from calculating the gradient. The population size and the number of iterations were set to 30 and
50, respectively.

Tableting data factory: make & test
The tableting data factory setup was built on an M6 tapped table spanning 200 × 200 cm2. R1 and
R2 have a reach of 850 and 820 mm and can carry up to 5 and 14 kg of load, respectively. The
FlexPTS (DEC Group, Switzerland) technology is used to dose the pre-mixed powder blend. The
quantity of the powder discharged from the dosing unit is volumetric based where the volume can
be adjusted by setting the height of a piston altering the powder chamber height. The dosing unit
collects the powder in the chamber using a vacuum, the powder is discharged using compressed
air. The controller of the dosing unit has the capability to change the duration of the vacuum pump
and the pressure of the compressed air to dispense powders with different physical properties, e.g.,
to consider variations in density, and particle size/shape.

The R1 gripper releases the TU on the weighing balance (Cole-Parmer PA-224I, United States)
that is placed under the dosing unit. The tube that carries the powder in TU has the capacity of
3000 mm3 (Supplementary Note 3.1.2). A sliding gate operated through a linear solenoid is used
to hold and then release the powder dosed from the dosing unit. To perform NIR measurements
for blend homogeneity assessment, the gate of the TU incorporates a 1-mm thick, 10 mm diameter
sapphire glass window. The round tube that holds the powder within TU has a diameter of 8 mm.
As this small tube can pose a challenge when discharging adhesive and cohesive materials, the
inside of the tube was coated with PTFE to create a non-stick surface and a motorised vibrator,
which is activated upon the opening of the gate. Two customised 3D printed fingers are mounted
on the robotic gripper that grasp the TU from the back to transport the powder to the different
stations (Supplementary Note 3.1.3). As the mass of the powder discharged into the TU is weighed
prior to tablet manufacturing, the electronic devices in the TU need to be electrically connected
with fingers through metallic touchpoints to allow the TU to remain connection-free and
standalone when it is placed on the weighing balance. Through this connection, the electric
solenoid and the vibrator are operated by an external and customised electronic control unit. The
control unit for the TU receives the control commands from the orchestration system via serial
communication and operates the solenoid and vibrator based on an external power supply. The
orchestration system also acquires the initial weight of the TU before getting the dose and subtracts
this weight from the final weight to determine the true value of powder obtained in that iteration.

A NIR spectrometer (Micro NIR PAT-W, VIAVI, United States) is incorporated in the workflow
to assess blend homogeneity. As changing ambient conditions may influence the NIR
measurement, new dark and reference scans need to be acquired over time. Therefore, a 99%
reflectance disc is attached to R1 to take the reference scan at the beginning of each iteration.

Tablets are produced using a compaction simulator (STYL’One Nano, MEDELPHARM, France).
The door of the compaction simulator was replaced by a laser curtain to provide the robotic arm
R1 easy access to the compaction die for powder discharge. The compaction simulator’s control
system halts the entire machine immediately if the laser curtain is interrupted. It then waits until
R1 has dispensed the powder and moved clear of the laser curtain before initiating the compaction
process. The tablet chute is kept within the boundaries of the laser curtain and linked with the
tablet tester through a side wall to avoid any process interruption.

A fully automated tablet tester (AT50, SOTAX, Switzerland) is used to measure breaking force,
weight, diameter, and thickness of the tablets. The testing process starts as a tablet enters the feeder
of the tester, where it moves through various stations to assess its properties. A bespoke TS is used
to differentiate between damaged and undamaged tablets. The flow of tablets is controlled by
adjusting the position of a motor-driven barrier (Supplementary Note 3.1.4). To ensure proper
alignment of the undamaged tablets, a linear solenoid pushes them onto their flat surface. Bespoke
robotic fingers (Supplementary Note 3.1.3) are mounted on the R2's robotic gripper to accurately
grip the tablet for transport.

The CU is attached to a high-power vacuum cleaner that is operated by an external control unit.
Operation of CU is signalled by the orchestration system, and it only runs when the TU requires
cleaning.

Tableting data factory: orchestration system
The orchestration system, designed in LabVIEW, can control and monitor all instruments remotely
(Supplementary Note 3.1.7). Each instrument is assigned a dedicated computer for two main
reasons: 1) to provide local access for users who may need to operate the instrument separately for
other tasks, and 2) to standardise diverse communication protocols into a unified protocol for
seamless communication with the orchestration system (as illustrated in Supplementary Note
3.1.7). The orchestration system also communicates with the experimental agent.

Tableting data factory: experimental agent
The compressibility model developed by Kawakita121,122 and the compactability model developed
by Ryshkewitch-Duckworth 85,123 were used in PIBO as the empirical models due to their proven
capability in capturing the compression profile 27:
𝜀 𝜀
0 0 (2)
𝜀(𝑃) = = 
𝑉 1+𝐵𝑃
1+( ∞)𝑏𝑃
𝑉
0
𝜎(𝜀) = 𝑇̂e−𝑘 b 𝜀 (3)
where 𝜀 is the porosity of the tablet, 𝜎 is the tensile strength of the tablet, and 𝑃 is the main
compression pressure. For the Kawakita model, 𝜀 is the initial powder bed porosity, 𝑉 is the net
0 ∞
volume of powder, 𝑉
0
is the initial apparent volume of powder, and 𝑏 is a tuning parameter which
is hypothesised to reflect the resistant and cohesive forces of the particles124. Following Tait et al.
27, the ratio of volumes 𝑉∞ and the constant 𝑏 were grouped into the single tuning parameter 𝐵 to
𝑉0
simplify the fitting process. For Ryshkewitch-Duckworth model, 𝑇̂ is the strength at zero porosity
and 𝑘 represents the material’s bonding capacity. The acquisition function in the classic (black-
b
box) Bayesian optimisation is constrained such that:
Err(𝑓 (𝑋),𝒟 ) ≤ Err(𝑓 (𝑋),𝒟 ) (4)
K 𝑛 K 𝑛−1
Err(𝑓 (𝑋),𝒟 ) ≤ Err(𝑓 (𝑋),𝒟 ) (5)
R−D 𝑛 R−D 𝑛−1
where 𝑓 and 𝑓 are the Kawakita and Ryshkewitch-Duckworth models, respectively; 𝒟 refers
𝐾 R−D 𝑛
to the dataset of collected experimental observations at iteration 𝑛; Err(𝑓(𝑋),𝒟 ) denotes the
𝑛
error (root mean squared error in this study) between the model and the collected data at iteration
𝑛. These constraints mean that while minimising the acquisition function from black-box BO, the
optimisation trajectory is confined to reduce the error between the collected data and models. The
optimisation is terminated if the change in the goodness of fit within the last two iteration falls
below a user-defined minimum threshold (in this study, set to be less than 20% change in the
tuning parameters fit, i.e., Err(𝑓 (𝑋),𝒟 ) and Err(𝑓 (𝑋),𝒟 ), as compared to the previous
K 𝑛 R−D 𝑛
iteration, i.e., Err(𝑓 (𝑋),𝒟 ) and Err(𝑓 (𝑋),𝒟 )). These criteria were established in the
K 𝑛−1 R−D 𝑛−1
acquisition function to ensure the tuning parameters of compressibility and compactability profiles
are calculated with sufficient accuracy, enabling reliable prediction of the profile across a
continuous range of input parameters.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The application of AI, digitalization, and automation holds immense potential to accelerate the development of new drugs. Research in this vital area can significantly impact the health of patients worldwide. This paper represents an important first step in developing a self-driving tableting station. The authors have demonstrated novel contributions in three main areas:\n1. Digital Formulation of Blends: This approach ensures the production of tablets with optimal quality attributes.\n2. Self-Driving Tableting Station: This system can orchestrate multiple tasks to convert blends into tablets efficiently.\n3. AI-Driven Control and Optimization: The use of AI model to control and optimize the tableting process.\nThese innovations make the manuscript worthy of publication in Nature Communications. However, before I can recommend its publication, I would like the authors to address a few key points listed below:\n1. Compatibility of Excipient and AI: The Digital Formulator is missing a crucial element—the compatibility of excipients with the new AI. This is the first step in designing a formulation and should be addressed before focusing on other attributes like blend flowability and tablet tensile strength.\n2. Scale-Up Challenges: The main challenge in the CMC development process is scaling up from bench scale to pilot and commercial scales. Powders often exhibit physical effects like over-lubrication, segregation, and flow issues such as jamming and arching, which may not be present at small scales. Typically, early-stage industrial development is carried out with an eye towards larger scale, and appropriate scale models are developed to bridge between scales. The formulation and process optimization approach presented here may be sub-optimal at a larger scale.\n3. Tablet Shapes, Thickness, and Tooling: The paper and supplementary material do not mention tablet shapes, thickness, and tooling used to manufacture tablets. Model predictions of process parameters like pre-compression and main compression pressures to achieve target tablet tensile strength and porosity will depend on the tablet shapes and tooling used. This information is crucial for accurate model predictions and should be included.\n4. Porosity Constraint: The porosity constraint (> 15%) used for in silico optimization is counterintuitive. It will end up over-constraining the space for feasible solutions. The usual industrial practice is to increase the solid fraction or reduce porosity until the desired tablet hardness or tensile strength is achieved.\n5. NIR Method Development:\no How much effort went into developing the NIR method for assessing blend homogeneity and tablet content uniformity? This should be included in Table 2.\no NIR spectra are known to depend on API particle size and blend bulk density. How was the baseline spectral drift corrected?\no Was the NIR model recalibrated to accommodate changes in API particle size and blend bulk density?\nMinor Points:\n1. On line 238, the mass fractions of CCS and MGST have been flipped.\n2. The correct Y-axis label for Figure S12(b) should be ‘Tablet Porosity (%)’.\n3. It is unclear if section 7.4 on ‘Extended Reality’ in the main paper adds any scientific value. It should be moved to the supplementary information to help reduce the length of the paper.\nAddressing these points will strengthen the manuscript and provide a clearer understanding of the platform's capabilities and limitations.", "reviewer_2": "This study presents an integrated platform for tablet formulation and manufacturing, built around a Digital Formulator and a Self-Driving Tableting DataFactory. This is an interesting study. However, the study may be better suitable for pharmaceutical journals.\nSeveral technical issues are to be solved.\n1. The dataset consists of 113 tablet formulations with 653 data points collected under different compression pressures. The authors employ deep neural network (DNN) models to predict the porosity and tensile strength of tablets containing previously unseen active pharmaceutical ingredients (APIs). Given the small size of the dataset and the complexity of the DNN algorithm, is there a risk of overfitting?\n2. The study only contains several drugs and excipients (e.g. MCC, lactose, CCS, MgS). Alghough these excipients are widely used in tablet formulations, there are much more other excipients in tablet formulations.\n3. How do the small-scale formulations transition to large-scale manufacturing batches?", "reviewer_3": "Authors report a detailed description of an approach for intensifying powder based product design with an example from pharmaceutical products. This piece of work is elegant engineering, programming and automation example, ultimately orchestrating powder handling flow in a (potentially) self-optimizing manner.\nHowever, the reported work remains very ‘developmental’ focusing on increasing the number of experiments that can be performed, with a limited focus on scientific questions! Current manuscript is focusing on an approach simply aiming to find a (local) optimum composition without asking the fundamental question ‘Why?’. Authors do not really go into using this approach to understand the physics of particulate systems better. What are the underlying reasons for powder flow, compaction of particles, strength of a compact – what is affecting charging of particles (particle surface) -> flow, what is the effect of particle size distribution, why does a compacted powder form a solid compact with a high enough tensile strength?\nPlease discuss this properly and strengthen this part in the manuscript.\nThe second major comment is the target product profile. What about the most critical property of a compacted pharmaceutical tablet for immediate release, disintegration? Authors do not mention disintegration nor measure any disintegration behavior (or very shortly, mainly pointing to ‘future studies should…’). It can be assumed that tablet porosity correlates with disintegration time, but this is quite far fetch with the current materials.\nPlease consider including this into the work properly.\nThird major comment: state of the art on self-optimizing labs is dominantly with liquid phase system (organic reaction optimization in flow reactors etc). Authors have a point in their work, and they have a clear novelty, because now they operate with powders, solid matter. This important aspect is not really spelled out for the reader.\nPlease compare and contrast properly this aspect of novelty in the Introduction chapter of the work.\nFourth one: authors refer to physics-based models. This is a strong expression for basically curve-fitting (empirical models of Kawakita and R-D). Here it is suggested that authors tune down the physics, especially considering that they are not really discussing the fundamental physics of powder flow and underarticulate interactions affecting tablet processes.\nMore minor comments, but still something that should be addressed:\nThe chemical space of both excipients and active pharmaceutical ingredients is quite limited. Basically few excipients (cellulose, lactose, calcium phosphate and mannitol) and six/eight simple APIs. Also, considering the chemical space: authors have an interface to Cambridge Structural Database (CSD). However, very few descriptors are used for eight materials. Considering that CCDC is advertising on their homepage that they have 1.25M accurate 3D structures, the author’s discussion seems very limited.\nConsidering powder flow and tablet compaction, it is well-known that particulate properties are of critical importance. As a continuation of the previous comment on ‘chemical space’, the ‘particulate space’ is quite limited: APIs are relatively large particle size and no really challenging APIs are included in the experimental data set.\nPlease comment on this and revise accordingly.\nParticle characteristics are described in Table S1. Particle size is reported with two digits (=10 nm), which is not realistic considering sampling error and the method used for this analysis. And at the end of the day, this is not even relevant for making powders flow and to compact them.\nStrengthen the description of particulate space, and discussion related to this.\nSame Table S1: textbook is teaching us that MCC Avicel PH101 is closer to 50 microns and PH102 is 100 microns. Here the values are opposite. This is most likely a typo, BUT raising a concern of the overall quality of the raw data. It is appreciated that data is available in the Source Repository, but readers should be able to trust the data in the times when everything is easy to make automated and just copy-paste the errors to the next generation of scientists.\nAuthors make it difficult to capture the composition of their model system. Line 239 is stating that there is 3.5 w% of MgSt (a really high amount), but Supporting info is saying 1 w%.\nSupporting info Line 28: formulated as presented in Table S1?? Please proofread your text.\nThroughout the text; authors refer to Figures in the main text and supporting information randomly without always indicating S for Figure in the supporting information. Please proofread the text carefully for that aspect.\nTable 2 in Conclusions seems to appear in a wrong place. It can be included in one of the earlier chapter as a discussion element, but it seems wrongly placed when in Conclusions.\nMinor minor comment: the use of abbreviation XR might confuse a reader with pharmaceutical background. This abbreviation is often use for extended release (XR) products. Here authors report work on immediate release. Otherwise, very elegant chapter and novel approach within the pharmaceutical community.\nReviewing the full code is outside my core expertise and would have taken too much time to do full validation of that. Please find someone with more data science background to do that part. I have read this work from a practical, pharmaceutical science, point of view.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_5": "Reviewer report for Nature Communications manuscript #NCOMMS-25-15811\nManuscript title: Accelerated Medicines Development using a Digital Formulator and a Self-Driving Tableting DataFactory\nIn this study, the Authors discussed an automated tableting platform that leverages machine learning, modelling tools such as physics-informed Bayesian optimization (PIBO), and robotic automation. The authors offered a material-to-product approach to predict and optimise critical quality attributes for different formulations, linking raw material attributes to tablet properties such as flowability, porosity, and tensile strength. The motivation for the work is important, namely to improve the sustainability and efficiency of direct compaction tablet drug product development. Substantial research and development efforts are noted and applauded for. However, the paper does not represent significant enough scientific innovation. The paper employed mostly established, or the author group’s previously established methods, and did not report any scientifically significant findings. While the time and material saving claims are noted, they lacked substantiation. The material and time saving by itself is considered as lack of scientific significance by the reviewer. Automation is naturally expected to speed things up as an incremental engineering advancement rather than scientific innovation. In addition, the writing of the manuscript is significantly below the standards of Nature Communications. The discussion is confusing to such an extent that the scientific quality of data is called into question. The reviewer provides the following review feedback with the intention of being as constructive as possible. The reviewer recommends the authors, after major revisions, to re-submit the manuscript to a more process engineering-oriented journal, potentially as 2 manuscripts. The reviewer recommends rejection of the manuscript from further publication consideration by Nature Communications.\nMajor concerns on Figures and Tables:\nSetting aside for a moment the content, the presentation clarity and quality of the majority of the figures and tables do not meet Nature Communications standards.\nFigure 1 was intended to clarify the overarching workflow. Instead, the reviewer found it to cause more confusion. Please clearly annotate and explain:\n1. The relationship among the blocks.\n2. Which blocks represent measurements, and which represent prediction.\n3. The relationship between shaded blocks and bullet texts without shade.\n4. The meaning of different greyscale shades of the shaded blocks, if any.\n5. The blocks under thick arrow – are they supposed to be the input or output to the main block (e.g., “Material Characterisation” – shouldn’t particle size be an output rather than an input?). If the arrows do not imply input, output or directionality, it should not be utilized.\n6. Figure 1C was referenced (line 308) but was not annotated in the figure.\n7. Why did “Tableting DataFactory” show up twice? Does Material Characterization only input to Digital Formulator, but not to Tableting DataFactory?\nIt is disappointing to the reviewer that the authors started a Nature Communications manuscript Figure with a pile of bullet points, indicating a lack of architectural deliberation, presentation clarity, or both.\nFigure 2 is an expansion of “Digital Formulator” from Figure 1.\n1. “Hybrid system of models” block is consistent with Figure 1, but “In-Silico Optimisation” is not. Optimization appeared several times in Figure 1, thus it is impossible to correspond and rationalize how these optimization modules in Figure 2 relate to that of Figure 1.\n2. Particle size appeared again several times in “Material (Powder) Space”, presumably as the input of “Mixture models”. How are they related to the “Material Charterisation” module in Figure 1?\n3. Similar question to Figure 1, what properties are measured, literature/database retrieved, or predicted?\n4. Is “New API characterization” part of the optimization loop, as the current figure suggests?\nFigure 3 – Is prediction accuracy only demonstrated on training data? If so, validation predictions on parameter space that were not part of the training should be shown to allow the reviewer to truly assess the model’s predictability. If the validation predictions are already in the plot (16 out of 113 as suggested in the method section), they should be clearly differentiated from the data used in the training. In Figure 3b, there is a range on the left of the histogram, >2.7MPa, that does not have any data. The corresponding range in the cross-correlation plot below showed a lot of data. Please explain. Why are the histograms in Figure 3 and Figure S1 different?\nFigure 4 – What is the purpose of rows with all zero values? Discuss or remove.\nFigure 5 needs major improvements. The robotic arm icons are very small and difficult to distinguish between #1 and #2. Consider bright colors such as Red and Blue that can help. The flow chart in panel (a) is difficult to decipher; the light gray arrows should have darker gray colors. In panel (b) some of the text is going outside the textbox, which is unacceptable. How is Figure 5 related to Figures 1 and 2? Consistent terminology and colour coding of blocks are essential to present a complex, layered workflow. SCU is referenced repeatedly in the text, but not annotated in the Figure. Modules are mentioned without any sequence, nor annotation, making the narration extremely difficult to follow.\nFigure 7 - This figure is overall poorly discussed. The caption referred to two tuning parameters as a and b, while there were no references of the four sub-figures a, b, c, and d. The figure showed a lot of data, however, was only briefly explained in line 402, which neither helped clarify the main point of the figure, nor offered explanation of the data. A plateau was referenced but was far from universal. Some clearly did not reach a plateau (Using Figure 7a as an example, red, orange, pink, and green did not reach a plateau). Why? Termination criteria were briefly discussed, but produced confusing results – why is red clearly not plateaued, but terminated, while grey, brown and green are plateaued in iterations #3 and #4 already, but not terminated and ran to iteration #5? Why does the parameters remain zero for both iterations #1 and #2, and for all the parameters? They cannot be simply explained as “underestimation of the initial predictions”. Rather, some systemic initialization issue is called into concern.\nFigure 8 is insufficiently discussed.\nFigure S2 is considered by the reviewer as one of the primary scientific findings from this manuscript. It is discussed extensively in the main manuscript body by the authors, while the figure is included as Supporting Information. The inconsistency is an ignorance at best. It should be included into the main manuscript. The discrepancy is substantial, and the explanation is insufficient. 2 out of 9 data points failed the FFC requirements, 4 under predicting, 5 over predicting, and some with error over 50%. This should be not claimed as “This consistency”.\nTable 2 should not be in the conclusion section. It needs to be completely redesigned for a journal publication. Table should focus on presenting the numbers, while the text should be incorporated into discussions. All numbers need to be substantiated with data from the paper or literature. For example, it is very difficult to believe the state of the art method is “manual filling” or “shoe feeder”. Shouldn’t an automated system intended for Nature Communications be compared with a state of art system used in the industry? If it is compared to a laboratory specific manual filling method, then the benchmark is not state of art, and the claims on wider industrial impact need to be revised. Further, to follow the author’s claims that the 72 tablets per hour rate included powder quality monitoring, tablet manufacturing, testing, storage and data handling, should the corresponding time using manual filling include those monitoring, testing, and handling elements as well? The table stated 72 tablets per hour, while the introductory section emphasized 1440 tablets in 24 hours, which is 60 tablet per hour. It is again inconsistency and ignorance at best.\nFundamental Modelling Concerns\n• Is 113 tablet formulations (653 data points) considered sufficient? The reviewer has doubt on the massive parameter space that can be representatively covered by 653 data points.\n• Can the authors comment how the 653 training data points cover the parameter space of the properties of the 9 formulations predicted? This comment links back to the training and validation comment for Figure 3. The reviewer appreciates the rich set of additional data included in “Support Information”. However, the reader should be able to understand the main points supported by the main manuscript. The supplementary document should be considered as optional, not mandatory.\n• What are importance scores of the input parameters? It would simplify the computation if only the relevant input parameters are retained in the modelling.\n• An ensemble of Deep Neural Network (DNN), Random Forest and Suport Vector Machine (SVM) were compared. However, the claim about DNN being the “superior” model is questionable. Yes, the SSE and R2 values in Table S3 favor DNN slightly. However, DNN tends to overfit and is less reliable in the context of the limited training data. How does the limited training set (only 653 data points to train a DNN for 21 features) impact the different models differently. For example, in Figure S1 tensile panel, there are 3 outliers near 3.2 MPa which does not exist for RF.\n• More fundamentally as a study design concern, if predictions were made using full set of input parameters including calculated particle informatics descriptors (Figure 3), DNN’s “superior performance” needs to be reevaluated against other models using the additional particle informatics descriptors.\n• Lines 223 onward lacks definitions on\no Ensemble learning\no Ensemble of models\no Exploration\no Exploitation and how it is different from exploration\nThe reviewer realizes some of this may be due to Nature Communication’s requirement on moving method section after conclusion. It is the authors’ responsibility to ensure narration coherency.\n• Line 232: Is the sub-set of excipients used for the training, but not the rest of excipients? If it is the case, the other excipients are not relevant and should be excluded from the manuscript entirely. If it is not, then the reviewer has further concerns on the authors’ choice in what data was included in the training, what was included in validation, and what was included for the optimization.\n• Line 235: Does the expanded list of excipients need to be included in the training?\n• Line 249: MCC1 did not have FFC data (Table S1). How does that impact MCC1 being chosen as an excipient?\n• Line 373: Porosity and tensile strength have been presented as primary prediction parameters. Why is elastic recovery introduced later? Either make it consistently part of the prediction parameters or elaborate on its delayed inclusion.\n• Line 463: GP is not defined. Figure S10 incorrectly referenced.\nMR/AR section\nThis section is both poorly written and poorly integrated. The argument of its innovation and demonstration of its value are both weak. While the reviewer appreciates it as an interesting technology demonstration, it does not belong to this manuscript in general and is very far away from Nature Communications standards.\nGrammar, language and consistency issues\nThese issues are throughout the manuscript with examples noted below,\n• In the Abstract: “This approach accelerates… tablet within 6 hours…” should be “tablet TO within 6 hours.”\n• On page 26 footnote: “The list of informatics descriptors IS”\n• Figure 5: To reduce clutter, the abbreviations PIBO and SuMoBO should be deleted. Keep capitalization consistent (“Orchestration” versus “transfer tablets.”)\n• Figure 6: Typo in panel (a) “DataFacotry”\n• In Table 2, “hoper” should be hopper.\n• There are typos in Figure S1, “SVR” should be SVM.\n• “Table 1” and “Table 1 in Supporting Information” should be better distinguished with Table S1. Similarly for other references to Tables and Figures.\n• The Abstract mentions less than 5 grams of API required, however it needs a direct comparison with the “state-of-the-art” e.g. >75% savings in materials. This would highlight the impact of the technology in easily understandable terms.\n• Line 506: The reviewer found the use of versions confusing and unnecessary. Given the excessive length of the manuscript, the reviewer suggests removal of any reference to “version”, and focus on the latest and most up to date results.\n• The word “DataFactory” in the title is confusing. It maybe best to drop it; “Self-Driving Tableter” would be enough.\n• Line 315 – what happened to the powder if they are neither diverted to waste nor reused? It is implied that it will be used by the process anyway. Clarify or remove.\n• Line 316 – How are the tablets not meeting uniformity criteria identified?\n• Line 358 – Is there a mode other than self-driving that the authors intended to compare?\n• Line 419 – Figures S8 and S9 have nothing to do with the context. Consequently, the comparison cannot be assessed by the reviewer.", "reviewer_author_response": "Response to Reviewers for “Accelerated Medicines\nDevelopment using a Digital Formulator and a Self-\nDriving Tableting DataFactory”\nFaisal Abbas1^ & Mohammad Salehian1^, Peter Hou1, Jonathan Moores1, Jonathan Goldie1,\nAlexandros Tsioutsios1, Theo Tait1, Victor Portela2, Quentin Boulay3, Roland Thiolliere3,\nAshley Stark4, Jean-Jacques Schwartz4, Jerome Guerin4, Andrew G. P. Maloney5, Alexandru\nA. Moldovan5, Gavin K. Reynolds6, Jérôme Mantanus7, Catriona Clark1, Paul Chapman2,\nAlastair Florence1, Daniel Markl1*\n^ Authors contributed equally.\n* Corresponding author; email: daniel.markl@strath.ac.uk\nStrathclyde, Glasgow, G1 1RD, UK\n& Development, Operations, AstraZeneca UK Limited, Macclesfield, SK10 2NA, UK\nWe would like to thank the reviewers for their interest in this work and for taking the time to\nthoroughly review the paper. All comments have been responded to, and the manuscript is\nrevised accordingly.\nText Colour Key:\nReviewers Comment\nAuthors Response\nChanges in Paper\nThe application of AI, digitalization, and automation holds immense potential to accelerate\nthe development of new drugs. Research in this vital area can significantly impact the health\nof patients worldwide. This paper represents an important first step in developing a self-\ndriving tableting station. The authors have demonstrated novel contributions in three main\nareas:\n1. Digital Formulation of Blends: This approach ensures the production of tablets with\noptimal quality attributes.\n2. Self-Driving Tableting Station: This system can orchestrate multiple tasks to convert\nblends into tablets efficiently.\n3. AI-Driven Control and Optimization: The use of AI model to control and optimize the\ntableting process.\nThese innovations make the manuscript worthy of publication in Nature Communications.\nHowever, before I can recommend its publication, I would like the authors to address a few\nkey points listed below:\nComment 1 (C1). Compatibility of Excipient and API: The Digital Formulator is missing a\ncrucial element—the compatibility of excipients with the new API. This is the first step in\ndesigning a formulation and should be addressed before focusing on other attributes like\nblend flowability and tablet tensile strength."}, "author_response": ""}]

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