DOI: 10.5061/dryad.0vt4b8h8x
title: Machine learning-assisted exploration of multidrug-drug administration regimens for organoid arrays

description: Combination therapies enhance the therapeutic effect of cancer treatment;
 however, identifying effective interdependent doses, durations, and
 sequences of multidrug administration regimens is a time- and
 labor-intensive task. Here, we integrated machine-learning, automation,
 and large microfluidic arrays of cancer spheroids or patient-derived
 organoids formed in a tissue-mimetic hydrogel to achieve drastic
 acceleration of the discovery of effective multidrug administration
 regimens. For the clinically approved drug combination, we discovered a
 sequential administration regimen leading to a substantial reduction in
 the total drug dose, in comparison with concurrent drug supply, both at
 comparable drug efficacy. For the drugs that are currently under clinical
 development, we found a synergistic effect of concurrently administered
 drugs and showed that the synergy diminishes for the sequential drug
 supply. The developed strategy holds promise for the discovery of
 effective combination therapies for advanced cancer treatment, including
 personalized chemotherapies.
description: # Machine learning-assisted exploration of multidrug-drug administration
 regimens for organoid arrays
 [https://doi.org/10.5061/dryad.0vt4b8h8x](https://doi.org/10.5061/dryad.0vt4b8h8x) ## Overview This dataset supports the study of optimizing multidrug administration regimens for organoid arrays using machine learning (ML)-guided strategies. The data include processed fluorescence imaging results representing cell viability in response to different drug combinations, administration sequences, and timing strategies. The primary goal of the study is to identify optimal drug sequences that improve therapeutic efficacy using organoid-based models and Bayesian optimization (BO). ## Description of the data and file structure The data was collected from the fluorescence images of spheroids and organoids in the microarrays. ## Data collection and processing Fluorescence images of spheroids and organoids cultured in microfluidic arrays were collected to quantify live/dead cells. These images were processed using: * **ImageJ** (NIH, USA) * **Custom Python software**: [Live-dead tool](https://github.com/yakavetsiv/Live-dead) ([DOI:10.5281/zenodo.14903369](https://doi.org/10.5281/zenodo.14903369)) Optimization of drug regimens was performed using: * **Gryffin optimization software** and experiment tracking: [ml-mf_chemo repository](https://github.com/yakavetsiv/ml-mf_chemo) ([DOI:10.5281/zenodo.14890340](https://doi.org/10.5281/zenodo.14890340)) --- ## Data access and additional sources * This dataset is hosted on Dryad: [https://doi.org/10.5061/dryad.0vt4b8h8x](https://doi.org/10.5061/dryad.0vt4b8h8x) * Additional files and code: * [Optimization results and code](https://doi.org/10.5281/zenodo.14890340) * [Live/dead image analysis tool](https://doi.org/10.5281/zenodo.14903369) ## File Structure and Contents Each folder corresponds to figures in the publication and contains experimental data used for machine learning-assisted optimization and validation of drug administration regimens. --- ### /Fig 3 – Sequential Drug Administration to MCF-7 Spheroids #### **Fig 3b – Variation in Cell Viability Across Experimental Generations** * **`gen`** – Generation/experimental series ID * **`sample`** – Sample ID within generation * **`cv_mean`** – Mean cell viability (%) from ≥3 independent experiments * **`cv_sd`** – Standard deviation of cell viability (%) * **`seq`** – Drug sequence identifier (e.g., a = 1-2-3, b = 1-3-2, etc.) #### **Fig 3c – Cell Viability for Tested Sequential Drug Schedules** * **`gen`** – Generation/experimental series ID * **`sample`** – Sample ID within generation * **`cv_exp`** – Experimental cell viability (%) from a single experiment (mean value of up to 100 spheroids) #### **Fig 3d – Validation of Best-Performing Drug Combinations** * **`name`** – Sample label: * `G3_7`: Simultaneous administration of sample 7 from generation 3 * `GS2_7`: Sequential administration of sample 7 from generation 2 * `Ctrl`: Control samples (no treatment) * **`cv_exp`** – Experimental cell viability (%) from a single experiment (mean value of up to 100 spheroids) --- ### /Fig 4 – Comparison of Sequential vs Individual Drug Administration #### **Fig 4a – Comparison of Simultaneous Multidrug vs Single-Drug Treatment** * **`name`** – Sample label: * `G3_7`: Concurrent 3-drug administration * `DOX_G3_7`: 1.25 µM DOX for 48h * `CPA_G3_7`: 94 µM CPA for 48h * `5FU_G3_7`: 105 µM 5-FU for 48h * `Ctrl`: Control samples (no treatment) * **`cv_exp`** – Experimental cell viability (%) from a single experiment (mean value of up to 100 spheroids) #### **Fig S4b – Comparison of Sequential Multidrug vs Single-Drug Treatment** * **`name`** – Sample label: * `GS2_7`: Sequential 3-drug administration * `DOX_GS2_7`: 1.25 µM DOX for 8h * `CPA_GS2_7`: 94 µM CPA for 8h * `5FU_GS2_7`: 105 µM 5-FU for 32h * `Ctrl`: Control sample (no treatment) * **`cv_exp`** – Experimental cell viability (%) from a single experiment (mean value of up to 100 spheroids) --- ### /Fig 6 – Sequential Drug Administration to PDOs #### **Fig 6b – Variation in Cell Viability Across Sequential Schedules** * **`gen`** – Generation/experimental series ID * **`sample`** – Sample ID * **`cv_mean`** – Mean cell viability (%) from ≥3 experiments * **`cv_sd`** – Standard deviation of cell viability * **`seq`** – Sequence of 2-drug administration (e.g., a = 1-2) #### **Fig 6c – Cell Viability in Tested Sequential Drug Schedules** * **`gen`** – Generation/experimental series ID * **`sample`** – Sample ID * **`cv_exp`** – Experimental cell viability (%) from a single experiment (mean value of up to 100 spheroids) #### **Fig 6d – Validation of Best Simultaneous vs Sequential Drug Combinations** * **`name`** – Sample label: * `G3_2`: Best-performing simultaneous combination * `GS2_2`: Best-performing sequential combination * `Ctrl`: Control * **`cv_exp`** – Experimental cell viability (%) from a single experiment (mean value of up to 100 spheroids) --- ### /Fig 7 – Validation of Drug Administration on PDOs #### **Fig 7a – Simultaneous Drug Treatment vs Individual Drugs** * **`name`** – Sample label: * `G3_2`: Best simultaneous combination * `OLA_G3_2`: 116 µM Olaparib for 96h * `IBET_G3_2`: 0.993 µM IBET-762 for 96h * `Ctrl`: Control * **`cv_exp`** – Experimental cell viability (%) from a single experiment (mean value of up to 100 spheroids) #### **Fig 7b – Sequential Drug Treatment vs Individual Drugs** * **`name`** – Sample label: * `GS2_2`: Best sequential 2-drug treatment * `OLA_GS2_2`: 116 µM OLA for 30h * `IBET_GS2_2`: 0.993 µM IBET-762 for 66h * `Ctrl`: Control * **`cv_exp`** – Experimental cell viability (%) from a single experiment (mean value of up to 100 spheroids) --- ### `/Supporting Data/` ### Fig S4a-b – Reference Cell Viability Graph & Protocol for Drug Efficacy Assessment * **`cv`** – Cell viability in % (0–100%) * **`green`** – Calcein fluorescence intensity (arbitrary units, a.u.), GFP channel * At 0% viability: background signal * At 100% viability: maximum signal * **`red`** – Propidium Iodide fluorescence intensity (a.u.), GFP channel * At 0% viability: maximum signal * At 100% viability: background * **`cells`** – Cell type: * `MCF-7` – human breast cancer cell line (panel A) * `dcbxto.58` – patient-derived breast cancer organoid (PDO) line (panel B) --- ### Fig S5a – Dose-Response Curves for MCF-7 Spheroids to Individual Drugs * **`drug`** – Drug label: * `DOX` = Doxorubicin * `5-FU` = 5-Fluorouracil * `CPA` = Cyclophosphamide * **`conc`** – Drug concentration (µM) * **`cv_mean`** – Mean cell viability (%) from ≥3 independent experiments * **`cv_sd`** – Standard deviation of viability (%) Note: The 10000 µM 5-FU sample was each tested in a single independent experiment; therefore, standard deviations are not available (NA). The data point was included solely to aid visualization of the dose–response trend and was not used in curve fitting. --- ### Fig S5b – Comparison of Live/Dead Assay and Ki-67 Proliferation Assay for DOX * **`drug`** – Drug label (same as above) * **`conc`** – Drug concentration (µM) * **`feas`** – Feasibility of preparation (`True` or `False`) * **`cv_exp`** – Experimental cell viability (%) from a single experiment (mean value of up to 100 spheroids) * **`mode`** – Assay type: * `ld` = Live/Dead assay * `ki67` = Proliferation marker using FITC-conjugated Ki-67 antibody --- ### Fig S7a-c – Concurrent Drug Administration to MCF-7 Spheroids * **`gen`** – Generation/experimental series ID * **`sample`** – Sample ID within generation * **`conc_dox`** – DOX concentration (µM) * **`conc_cpa`** – CPA concentration (µM) * **`conc_5-fu`** – 5-FU concentration (µM) * **`cv_mean`** – Mean cell viability (%) from ≥3 independent experiments * **`cv_sd`** – Standard deviation of cell viability * **`ci_mean`** – Combination Index (CI) mean from ≥3 independent experiments * **`ci_sd`** – Standard deviation of CI --- ### Fig S9b – Total Drug Dose in Sequential vs Simultaneous Schedules (MCF-7) * **`gen`** – Generation/experimental series ID * **`sample`** – Sample ID * **`seq`** – Drug sequence (e.g., a = 1-2-3) * **`dox_dose`** – DOX dose (%) relative to simultaneous schedule * **`cpa_dose`** – CPA dose (%) * **`5-fu_dose`** – 5-FU dose (%) * **`dose`** – Total relative dose (%) of 3-drug combination compared to simultaneous administration --- ### Fig S13 – Dose-Response for PDOs Treated with OLA or IBET-762 * **`drug`** – Drug name: `OLA` (Olaparib), `IBET-762` * **`Conc`** – Drug concentration (µM) * **`cv_mean`** – Mean cell viability (%) from ≥3 independent experiments * **`cv_sd`** – Standard deviation of cell viability Note: The 100 µM and 2000 µM IBET-762 samples were each tested in a single independent experiment; therefore, standard deviations are not available (NA). These data points were included solely to aid visualization of the dose–response trend and were not used in curve fitting. --- ### Fig S14a-c – Concurrent Drug Administration to PDOs * **`gen`** – Generation/experimental series ID * **`sample`** – Sample ID * **`conc_ola`** – OLA concentration (µM) * **`conc_ibet762`** – IBET-762 concentration (µM) * **`cv_mean`** – Mean cell viability (%) from ≥3 independent experiments * **`cv_sd`** – Standard deviation of cell viability * **`ci_mean`** – Combination index (CI) mean from ≥3 independent experiments * **`ci_sd`** – Standard deviation of CI --- ### Fig S15a-b – Sequential Drug Schedules for OLA/IBET-762 (PDOs) * **`gen`** – Generation/experimental series ID * **`sample`** – Sample ID * **`seq`** – Sequence of 2-drug administration (e.g., a = 1-2) * **`ola_time`** – Duration of OLA administration (hours) * **`ibet_time`** – Duration of IBET-762 administration (hours) * **`dose`** – Total dose (%) compared to simultaneous administration ## Variable Definitions | Variable | Description | | -------------- | ---------------------------------------------------------------------------------------------------- | | `gen` | Generation/experimental series ID | | `sample` | Sample ID within each generation or experimental batch | | `cv_mean` | Mean cell viability (%) from at least 3 independent experiments | | `cv_sd` | Standard deviation of cell viability (%) across experiments | | `cv_exp` | Experimental viability (%) from a single experiment (mean of up to 100 spheroids) | | `seq` | Drug sequence identifier (e.g., a = 1-2-3, b = 1-3-2, etc.) | | `name` | Label for experimental condition (e.g., G3\_7, GS2\_7), includes drug combination and admin strategy | | `green` | Calcein fluorescence intensity (a.u.) – indicates live cells (GFP channel) | | `red` | Propidium Iodide fluorescence intensity (a.u.) – indicates dead cells (GFP channel) | | `cells` | Cell type used in the assay (e.g., MCF-7 breast cancer or dcbxto.58 breast PDOs) | | `drug` | Drug name (e.g., DOX = Doxorubicin, 5-FU = 5-Fluorouracil, CPA = Cyclophosphamide, OLA, IBET-762) | | `conc` | Drug concentration in micromolar (µM) | | `feas` | Feasibility of dilution – `True` if feasible, `False` if not | | `mode` | Assay mode: `ld` = live/dead staining, `ki67` = Ki-67 proliferation marker | | `conc_dox` | Doxorubicin concentration in µM | | `conc_cpa` | Cyclophosphamide concentration in µM | | `conc_5-fu` | 5-Fluorouracil concentration in µM | | `conc_ola` | Olaparib concentration in µM | | `conc_ibet762` | IBET-762 concentration in µM | | `ci_mean` | Mean combination index from a single experiment | | `dox_dose` | Relative dose of Doxorubicin used in the schedule (%) | | `cpa_dose` | Relative dose of Cyclophosphamide used (%) | | `5-fu_dose` | Relative dose of 5-FU used (%) | | `dose` | Total relative drug dose (%) compared to simultaneous administration | | `ola_time` | Duration of Olaparib administration in hours | | `ibet_time` | Duration of IBET-762 administration in hours | ## Abbreviations and Notes * **ML**: Machine Learning * **BO**: Bayesian Optimization * **PDO**: Patient-Derived Organoid * **MCF-7**: Human breast cancer cell line * **DOX**: Doxorubicin * **CPA**: Cyclophosphamide * **5-FU**: 5-Fluorouracil * **OLA**: Olaparib * **IBET-762**: BET bromodomain inhibitor * **CI**: Combination Index (CI &lt; -0.1: synergy, CI = 0: additive effect, CI &gt; 0.1: antagonism) * **a.u.**: Arbitrary Units * **G3_X / GS2_X**: Sample X from Generation 3 (G = simultaneous, GS = sequential schedule) * **Live/Dead Assay**: Fluorescent viability assay using Calcein-AM (live, green) and Propidium Iodide (dead, red) [Live-dead tool](https://github.com/yakavetsiv/Live-dead) ([DOI:10.5281/zenodo.14903369](https://doi.org/10.5281/zenodo.14903369)) ## Access information Other publicly accessible locations of the data: * [https://doi.org/10.5281/zenodo.14890340](https://doi.org/10.5281/zenodo.14890340)