The Use of Physiologically Based 
Pharmacokinetic Analyses  
Biopharmaceutics Applications for Oral 
Drug Product Development, 
Manufacturing Changes, and Controls
 
Guidance for Industry 
 
 
 
DRAFT GUIDANCE
 
 
  This guidance document is being distributed for comment purposes only. 
 
Comments and suggestions regarding this draft document should be submitted within 60 days of 
publication in the Federal Register of the notice announcing the availability of the draft 
guidance.  Submit electronic comments to https://www.regulations.gov.  Submit written 
comments to the Dockets Management Staff (HFA-305), Food and Drug Administration, 5630 
Fishers Lane, Rm. 1061, Rockville, MD  20852.  All comments should be identified with the 
docket number listed in the notice of availability that publishes in the Federal Register. 
 
For questions regarding this draft document, contact Paul Seo at 301-796-4874.  
 
 
 
 
 
 
 
 
 
 
 
U.S. Department of Health and Human Services 
Food and Drug Administration 
Center for Drug Evaluation and Research (CDER) 
 
October 2020 
Pharmaceutical Quality/CMC
19190003dft.docx 
09/28/20   
The Use of Physiologically Based 
Pharmacokinetic Analyses  
Biopharmaceutics Applications for Oral 
Drug Product Development, 
Manufacturing Changes, and Controls 
Guidance for Industry 
 
 
 
Additional copies are available from: 
 
Office of Communications, Division of Drug Information 
Center for Drug Evaluation and Research 
Food and Drug Administration 
10001 New Hampshire Ave., Hillandale Bldg., 4th Floor  
Silver Spring, MD  20993-0002  
Phone: 855-543-3784 or 301-796-3400; Fax: 301-431-6353; Email: druginfo@fda.hhs.gov 
https://www.fda.gov/drugs/guidance-compliance-regulatory-information/guidances-drugs 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
U.S. Department of Health and Human Services 
Food and Drug Administration 
Center for Drug Evaluation and Research (CDER) 
 
October 2020 
Pharmaceutical Quality/CMC 
 
  Contains Nonbinding Recommendations 
Draft  Not for Implementation 
TABLE OF CONTENTS 
 
I.  INTRODUCTION............................................................................................................. 1 
II.  BACKGROUND ............................................................................................................... 2 
III.  IMPLEMENTATION OF PBPK MODELING FOR BIOPHARMACEUTICS 
APPLICATIONS .............................................................................................................. 3 
IV.  DEVELOPMENT AND EVALUATION OF PBPK MODELS FOR 
BIOPHARMACEUTICS APPLICATIONS .................................................................. 5 
A.  General Strategy ............................................................................................................................ 5 
B.  General Considerations ................................................................................................................. 7 
1. Model Objective(s) ........................................................................................................................... 7 
2. Model Development ......................................................................................................................... 7 
3. Model Validation and Refinement ................................................................................................... 8 
V.  PBPK MODELING FOR BIOPHARMACEUTICS APPLICATIONS TO SUPPORT 
PRODUCT QUALITY ......................................................................................................... 9 
A.  Development of Clinically Relevant Dissolution Specifications (Method and Acceptance 
Criteria) .......................................................................................................................................... 9 
1. Aid in Biopredictive Dissolution Method Development ................................................................... 9 
2. Support Clinically Relevant Dissolution Acceptance Criteria....................................................... 10 
B.  Establishment of Clinically Relevant Drug Product Quality Specifications (Other Than 
Dissolution) ................................................................................................................................... 11 
C.  Quality Risk Assessment for Pre- and Postapproval Changes and Risk-Based Biowaivers . 12 
GLOSSARY................................................................................................................................. 14 
REFERENCES ............................................................................................................................ 16 
  Contains Nonbinding Recommendations 
Draft  Not for Implementation 
1  The Use of Physiologically Based Pharmacokinetic Analyses  
2  Biopharmaceutics Applications for Oral Drug Product 
3  Development, Manufacturing Changes, and Controls 
4  Guidance for Industry1 
5   
6   
7   
8   
9  This draft guidance, when finalized, will represent the current thinking of the Food and Drug 
10  Administration (FDA or Agency) on this topic.  It does not establish any rights for any person and is not 
11  binding on FDA or the public.  You can use an alternative approach if it satisfies the requirements of the 
12  applicable statutes and regulations.  To discuss an alternative approach, contact the FDA staff responsible 
13  for this guidance as listed on the title page.   
14   
15   
16   
17   
18  I.  INTRODUCTION  
19   
20  This guidance provides general recommendations regarding the development, evaluation, and 
21  use of physiologically based pharmacokinetic (PBPK) analyses for biopharmaceutics 
22  applications employed by sponsors of investigational new drug applications, and applicants for 
23  new drug applications, or abbreviated new drug applications, and supplements to these 
24  applications,2,3 for oral drug product development, manufacturing changes, and controls. PBPK 
25  analyses use models and simulations that combine physiology, population, and drug substance 
26  and product characteristics to mechanistically describe the pharmacokinetic (PK) and/or 
27  pharmacodynamic behaviors of a drug product.4  
28   
29  The application of PBPK modeling in support of drug product development is an evolving field. 
30  We note that there are multiple terms used to describe PBPK analyses for biopharmaceutics 
31  applications, including PBPK absorption modeling (Zhang et al. 2017), physiologically based 
32  absorption modeling (Kesisoglou et al. 2016), and physiologically based biopharmaceutics 
33  modeling (PBBM) (Heimbach et al. 2019). This guidance uses the term PBPK analyses (or 
                                                 
1 This guidance has been prepared by the Office of Pharmaceutical Quality in the Center for Drug Evaluation and 
Research at the Food and Drug Administration.  
 
2 For the purposes of this guidance, sponsor refers to sponsors of investigational new drug applications, applicants 
for new drug applications and abbreviated new drug applications, and supplements to those applications. 
 
3 The scientific principles described in this guidance are applicable regardless of whether an original clinical study 
demonstrated bioavailability/bioequivalence and are relevant whether or not an application is required. 
 
4 Submission of PBPK analyses to FDA is discussed in the guidance for industry Physiologically Based 
Pharmacokinetic Analyses  Format and Content (August 2018). We update guidances periodically. For the most 
recent version of a guidance, check the FDA guidance web page at https://www.fda.gov/regulatory-
information/search-fda-guidance-documents. 
  1  Contains Nonbinding Recommendations 
Draft  Not for Implementation 
34  modeling) for biopharmaceutics applications to emphasize the focus on drug product quality 
35  attributes and a mechanistic understanding of their interaction with physiology to affect in vivo 
36  drug performance.  
37   
38  This guidance applies only to orally administered, systemically active drug products. It does not 
39  apply to locally acting drug products, including orally delivered gastrointestinal (GI) drug 
40  products that reach the site of action before entering systemic circulation. The use of PBPK 
41  analyses for biopharmaceutics applications for locally acting drug products will be considered on 
42  a case-by-case basis and via communication with FDA.  
43   
44  In general, FDAs guidance documents do not establish legally enforceable responsibilities.  
45  Instead, guidances describe the Agencys current thinking on a topic and should be viewed only 
46  as recommendations, unless specific regulatory or statutory requirements are cited. The use of 
47  the word should in Agency guidances means that something is suggested or recommended, but 
48  not required.  
49   
50   
51  II.  BACKGROUND 
52   
53  Several guidances for industry advocate the use of biopharmaceutics tools,5 such as in vitro 
54  dissolution, and in vivo bioavailability (BA)/bioequivalence (BE) studies, along with modeling 
55  approaches to support drug product quality. In addition, quality by design (QbD) principles 
56  recognize that drug product quality cannot be tested into drug products; quality should be built 
57  into drug products by design.6 In this regard, QbD enables an in-depth understanding of the 
58  relationship among critical quality attributes (CQAs), critical material attributes (CMAs), critical 
59  process parameters (CPPs), and predefined clinical performance metrics (e.g., systemic exposure 
60  such as C  and area under the curve (AUC)). Data describing this relationship are essential for 
max
61  establishing an in vitro-in vivo link. Establishing an in vitro-in vivo link supports clinically 
62  relevant drug product specifications. 
63   
                                                 
5 See the guidances for industry Dissolution Testing of Immediate Release Solid Oral Dosage Forms (August 1997), 
Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In Vitro/In Vivo Correlations 
(September 1997), and Immediate Release Solid Oral Dosage Forms Scale-Up and Postapproval Changes: 
Chemistry, Manufacturing, and Controls, In Vitro Dissolution Testing, and In Vivo Bioequivalence Documentation 
(November 1995).  
 
6 See the ICH guidance for industry Q8(R2) Pharmaceutical Development (November 2009). 
 
  2  Contains Nonbinding Recommendations 
Draft  Not for Implementation 
64  Although the pharmaceutical industry has in some cases been successful in developing in vitro/in 
65  vivo correlations (IVIVCs) to support biowaiver7 requests in lieu of in vivo BE studies for major 
66  manufacturing changes (Nguyen et al. 2017), development of an adequate IVIVC for regulatory 
67  submission remains challenging (Suarez-Sharp et al. 2016). FDA recognizes this challenge and 
68  encourages the development and use of new tools and approaches for linking pharmaceutical 
69  quality to clinical performance. Advances in modeling and simulation have enabled the 
70  integration of factors such as the physicochemical properties of the active pharmaceutical 
71  ingredient (API), dissolution data, and the physiology of the GI tract into the development of 
72  PBPK models. As such, PBPK modeling has become a promising tool in predicting systemic 
73  drug exposure (Kostewicz et al. 2014a) and has been used for dose selection, food effect 
74  assessment, and drug interaction potential evaluation (Wagner et al. 2015a; Wagner et al. 2016; 
75  Huang et al. 2013; Wagner et al. 2015b).   
76   
77   
78  III.  IMPLEMENTATION OF PBPK MODELING FOR BIOPHARMACEUTICS 
79  APPLICATIONS 
80   
81  The application of PBPK modeling could be expanded to pharmaceutical drug product 
82  development, manufacturing changes, and controls. One feasible approach is to combine an in 
83  vitro drug product test (e.g., biopredictive dissolution) with PBPK models where in vitro 
84  dissolution data provide input to predict absorption (Heimbach et al. 2019). As such, dissolution 
85  testing is a key modeling input, because it probes both the extent and rate of in vivo drug product 
86  release.  
87   
88  The purpose of PBPK analyses for biopharmaceutics applications is to combine dissolution 
89  modeling/biopredictive dissolution or other in vitro testing inputs with PBPK modeling strategies 
90  to quantitatively describe (or characterize) the potential interactions of formulation variants with 
91  the body and their effect on drug exposure. This modeling approach should include relevant 
92  mechanisms pertaining to the absorption process, such as GI tract local metabolism (if 
93  applicable) and drug transport, and incorporate drug product quality properties to predict 
94  systemic drug exposure.  
95   
                                                 
7 In addition to waiver of an in vivo BE requirement under 21 CFR 320.22, there are certain circumstances in which 
BE can be evaluated using in vitro approaches under 21 CFR 320.24(b)(6). The scientific principles described in this 
guidance regarding waiver of an in vivo requirement also apply to consideration of in vitro data under that 
regulation. In such circumstances, an in vivo data requirement is not waived, but rather, FDA has determined that in 
vitro data is the most accurate, sensitive, and reproducible approach for establishing BE, as required under 21 CFR 
320.24(a). Nonetheless, for ease of the reader, this guidance refers to either the decision to waive an in vivo BE 
requirement under 21 CFR 320.22 or the decision to accept in vitro BE data in accordance with 21 CFR 320.24(a) as 
a biowaiver. 
  3  Contains Nonbinding Recommendations 
Draft  Not for Implementation 
96  With these mechanistic elements defined, PBPK modeling for biopharmaceutics applications 
97  could predict the effect of variations from the CMAs, CPPs, and CQAs on drug exposure toward 
98  the establishment of a safe space via either IVIVCs or in vivo-in vitro relationships combined 
99  with virtual BE. A safe space (Abend 2018) is defined by the boundaries demarcated by in vitro 
100  specifications (i.e., dissolution or, when applicable, other relevant drug product quality 
101  attributes), within which drug product variants are anticipated to be bioequivalent to one another. 
102  Less optimally, but still possible (e.g., for modified-release (MR) formulations with appropriate 
103  additional supporting data), safe space represents specifications within which drug product 
104  variants are anticipated to be bioequivalent to the pivotal clinical batch(es).8 Building a safe 
105  space may also reduce the need for in vivo data to support regulatory assessment.9 Although safe 
106  spaces can be used for new and generic drug products, building a safe space for a generic drug 
107  product necessitates the identification of a range of virtual dissolution profiles within which the 
108  proposed drug products are found to be bioequivalent to one another and to the reference or 
109  target drug product (e.g., via virtual BE analysis). Also, the range of virtual dissolution profiles 
110  should contain the target (i.e., biobatch or pivotal clinical batch) dissolution profile.  
111   
112  The implementation of PBPK analyses for biopharmaceutics applications to support drug 
113  product quality should consider a risk-based approach (e.g., Kuemmel et al. 2020) and 
114  contemplate several factors such as: (1) whether in vivo dissolution (as opposed to permeability) 
115  is the rate-limiting step toward drug absorption; (2) the in vitro and in vivo data collected to 
116  develop, verify, and validate10 the proposed model; and (3) the complexity of the drug product 
117  formulation. For example, the use of PBPK analyses for biopharmaceutics applications to 
118  support major manufacturing changes for immediate-release (IR) drug products containing high 
119  solubility APIs generally is not warranted.11  
120   
121   
                                                 
8 If a clinical investigation (i.e., any experiment other than a BA study in which a drug is administered or dispensed 
to, or used on, human subjects) is necessary to demonstrate the safety or effectiveness of a proposed drug product, 
generally this type of study goes beyond the scope of information that may be relied upon as necessary for approval 
in an abbreviated new drug application (see 21 CFR 314.108(a) and the guidance for industry Determining Whether 
to Submit an ANDA or a 505(b)(2) Application (May 2019)).  However, for ease of the reader, use of the term 
clinical in this guidance may refer to clinical investigations conducted to support the demonstration of safety or 
effectiveness in a drug product submitted in a new drug application, or to in vivo studies submitted to support a 
demonstration of BE or other requirements under section 505(j) of the Federal Food, Drug, and Cosmetic Act and 
FDAs implementing regulations.   
 
9 See for example 21 CFR 320.25(a) ([t]he basic principle in an in vivo bioavailability study is that no unnecessary 
human research should be done). 
 
10 In the most general terms, verification refers to an assessment of model components, for example by examining 
computer codes and equations, to evaluate whether they accurately implement model assumptions; and validation 
refers to an assessment of the model performance in comparison with observed in vivo data.  
 
11 See the guidance for industry Dissolution Testing and Acceptance Criteria for Immediate-Release Solid Oral 
Dosage Form Drug Products Containing High Solubility Drug Substances (August 2018). 
 
  4  Contains Nonbinding Recommendations 
Draft  Not for Implementation 
122  IV.  DEVELOPMENT AND EVALUATION OF PBPK MODELS FOR 
123  BIOPHARMACEUTICS APPLICATIONS 
124   
125  A.  General Strategy 
126   
127  The general recommended process of developing (Zhang et al. 2011), evaluating, and applying a 
128  PBPK model for biopharmaceutics applications for an oral dosage form is presented in Figure 1 
129  and described in this section and section V., PBPK Modeling for Biopharmaceutics Applications 
130  to Support Product Quality.   
131   
132  A complete study report of the modeling and simulation work using a PBPK model for 
133  biopharmaceutics applications should be submitted to FDA for evaluation and included in the 
134  electronic common technical document Module 5.3.1.3.12  
135   
                                                 
12 When such a model is used for other purposes, other modules may be more appropriate. For additional 
information, see the guidance for industry Providing Regulatory Submissions in Electronic Format  Certain 
Human Pharmaceutical Product Applications and Related Submissions Using the eCTD Specifications (February 
2020). 
  5  Contains Nonbinding Recommendations 
Draft  Not for Implementation 
136  Figure 1. Recommended Workflow Describing Development and Evaluation of a PBPK 
137  Model for Biopharmaceutics Applications  
Identification of Model Objective(s) 
(Section IV.B.1) 
Model Development 
(Section IV.B.2) 
Structure  Parameters  Assumptions 
t 
Data collection/integration  n
e
m
Model Verification 
e ) 
n 3
i .
f B
e
R V.
l  (I
e
d
Model Validation   o
M
(Section IV.B.3) 
Acceptable?  No 
Yes 
Model Application 
(Section V) 
138   
139   
140  The data needed for model development include, but are not limited to:  
141   
142    Drug data comprised of drug substance physicochemical properties; formulation 
143  attributes; the drug product release mechanism; the absorption, distribution, metabolism, 
144  and excretion properties of the drug product; as well as other relevant clinical data (e.g., 
145  BA/BE or other PK data)  
146   
147    System data (i.e., anatomical structure and physiological parameters) for the GI tract and 
148  other organs and/or tissues, if applicable  
  6  Contains Nonbinding Recommendations 
Draft  Not for Implementation 
149   
150    Study design data (e.g., the tested drug product or formulation information, dosing 
151  regimen, and study subject population) of the in vivo studies selected for model 
152  development and validation.  
153   
154  B.  General Considerations  
155   
156  The following subsections provide general considerations for model development and evaluation 
157  in supporting pharmaceutical quality. 
158   
159  1.  Model Objective(s) 
160   
161  The specific drug product quality issue(s) or question(s) to be addressed by PBPK modeling for 
162  biopharmaceutics applications should be clearly described in the study report. Sponsors should 
163  provide an analysis of how the specified quality issue(s) or question(s) affect the PK 
164  performance of the drug product, the rationale for conducting the modeling and simulation, as 
165  well as any strategies undertaken to mitigate the risk of the change to PK performance. The 
166  analysis should also incorporate a description of the level of confidence in the modeling outcome 
167  based on additional data available to support the verification and/or validation of the model, and 
168  other factors, such as the model application, the therapeutic indication, and the therapeutic 
169  window of the drug. FDA will evaluate on a case-by-case basis the adequacy of the model for the 
170  intended purpose and data sufficiency for model verification and/or validation.   
171   
172  2.  Model Development 
173   
174  Model development should consist of the following three general elements. 
175   
176  a.  Model structure 
177   
178  The model structure should provide a mechanistic framework of drug oral absorption by 
179  representing the in vivo drug absorption process and accounting for the relevant product quality 
180  attribute(s) that affect drug dissolution and absorption. The construction of an absorption model 
181  should consider the model objective(s), as well as multiple factors affecting drug dissolution and 
182  absorption and their interactions. These factors include but are not limited to: physicochemical 
183  properties of the drug substance; formulation/process characteristics; drug release mechanism; in 
184  vivo drug dissolution process; supersaturation and precipitation processes; location and duration 
185  of absorption; drug permeation and transport pathway; and the effect of GI tract physiology on 
186  absorption. Finally, model construction should consider the supporting data and knowledge 
187  available to justify the model structure. Sponsors should also document the approaches taken to 
188  integrate quality attributes, such as dissolution, and other factors into the model.  
189   
190  Because the focus of the model is on in vivo dissolution and absorption, it is appropriate to 
191  combine a mechanistic absorption model with a simplified disposition model (e.g., a classic 
192  compartmental PK model or a reduced PBPK model that lumps tissue/organ compartments) for 
193  the prediction of systemic exposure following absorption. Such simplification is recommended if 
194  it does not compromise the ability to adequately describe processes governing the drug BA. 
  7  Contains Nonbinding Recommendations 
Draft  Not for Implementation 
195  When drug disposition involves complexity (e.g., nonlinear PK, saturation of clearance 
196  pathway), we recommend an alternative approach, such as incorporating enzyme kinetics in the 
197  disposition model. Any modification to the initial model structure should include sufficient 
198  justification (e.g., the addition of structural elements may be supported by comprehensive 
199  sensitivity analyses and/or appropriate proof that clearly demonstrates the significance of the 
200  metrics of interest).  
201   
202  b.  Model assumptions 
203   
204  The assumptions that underly the model structure and parameters should be clearly presented 
205  (e.g., the assumptions made upon drug disintegration, dissolution, precipitation, degradation, 
206  transport, first-pass effect, distribution, and clearance). The assumptions should be scientifically 
207  justified with supportive information and data, when available. The effect of these assumptions 
208  on model structure and/or parameter(s) should be described.  
209   
210  c.  Model parameters 
211   
212  The approach taken to incorporate drug product quality attributes into the model and the 
213  selection of parameters and parameter values as model inputs should be clearly presented and 
214  scientifically justified. Selection and evaluation of CMAs (such as drug substance 
215  physicochemical properties and excipient(s) level), CPPs (such as compression force), and CQAs 
216  (such as hardness, disintegration, and in vitro dissolution) as model inputs should consider 
217  whether these attributes and parameters can affect drug in vivo dissolution and absorption. 
218   
219  3.  Model Validation and Refinement 
220   
221  The predictive performance of a model should be validated for its intended purpose. Depending 
222  on the clinical risk and the intended purpose, the amount and type of data needed for model 
223  validation may vary. Independent datasets not used in model development are recommended to 
224  evaluate the predictive performance of the model. In general, for addressing pharmaceutical 
225  development and quality issues, the adequacy of the model to predict the effect of model inputs 
226  on the PK performance of the studied drug product should be demonstrated by establishing a 
227  clear rank-order relationship between in vitro testing (e.g., in vitro release/dissolution) and in 
228  vivo PK study results.  
229   
230  To increase confidence in the model, we strongly recommend that sponsors demonstrate the 
231  models predictive performance based on PK data from batches exhibiting unacceptable BA, in 
232  addition to those that exhibited acceptable BA (compared to a target and/or reference product). 
233  In this context, BA would be considered unacceptable when, based on BE criteria, the 90 percent 
234  confidence interval of the test-to-reference geometric mean ratio of C  and AUC fall outside 
max
235  the range of 80 to 125 percent. Model validation acceptance criteria should be established a 
236  priori and the criteria should be appropriate for the specified application. For instance, the 
237  acceptance criteria for a mechanistic IVIVC model to support biowaiver should comply with the 
238  criteria provided in the guidance for industry Extended Release Oral Dosage Forms: 
239  Development, Evaluation, and Application of In Vitro/In Vivo Correlations. 
240   
  8  Contains Nonbinding Recommendations 
Draft  Not for Implementation 
241  To demonstrate model predictive performance, sponsors should provide graphical and numerical 
242  comparisons of the predicted and observed in vivo drug concentrations (e.g., in plasma) versus 
243  time profiles as well as PK parameter estimates (e.g., C , T , and AUC) and statistical 
max max
244  analysis of those estimates (e.g., confidence intervals). Any significant deviation of the model 
245  prediction from clinical PK observation (e.g., failure to meet pre-defined model acceptance 
246  criteria) will be subject to evaluation by FDA.  
247   
248  When model refinement or optimization is necessary, we recommend uncertainty analyses on 
249  model structure and parameters. Such analyses can be performed by reevaluation of model 
250  assumptions and/or parameter sensitivity analysis. The model structure and/or parameters that 
251  are modified should be clearly presented and scientifically justified. When a model parameter is 
252  optimized, sponsors should provide, in addition to the scientific justification(s) and rationale, the 
253  selected initial values and range of parameters, the estimation method and optimization 
254  algorithm, and the in vitro and in vivo data used for optimization.  
255   
256   
257  V.  PBPK MODELING FOR BIOPHARMACEUTICS APPLICATIONS TO 
258  SUPPORT PRODUCT QUALITY  
259   
260  The in vivo prediction capability provided by PBPK modeling for biopharmaceutics applications 
261  allows for a wide array of uses in the pharmaceutical industry, including formulation 
262  development, biopredictive dissolution method development, clinically relevant product 
263  specifications setting, quality risk assessment, and drug product life cycle management. The 
264  implementation of PBPK modeling for biopharmaceutics applications may reduce the number of 
265  in vivo BA/BE studies (e.g., due to formulation and/or manufacturing process changes) 
266  conducted during the initial approval process, as well as support product scale-up and 
267  postapproval changes (SUPAC). The major regulatory uses of PBPK models for 
268  biopharmaceutics applications with respect to supporting product quality are presented below. 
269  For cases not discussed in this guidance, sponsors are encouraged to contact FDA.   
270   
271  A.  Development of Clinically Relevant Dissolution Specifications (Method and 
272  Acceptance Criteria) 
273   
274  1.  Aid in Biopredictive Dissolution Method Development  
275   
276  Although progress has been made with the use of biorelevant media and appropriate testing 
277  conditions to create physiologically based dissolution methods (Kostewicz et al. 2014b), the use 
278  of in vitro dissolution data to quantitatively predict drug absorption is challenging. A 
279  biopredictive dissolution method can be used to generate a dissolution profile. This profile can 
280  be used to predict systemic exposure after oral administration of solid dosage forms (Suarez-
281  Sharp et al. 2018). The dissolution data could be effectively used as a surrogate to assess the 
282  clinically relevant effect of drug product variants, thereby streamlining drug product 
283  development.  
284   
285  We encourage development of biopredictive dissolution methods at the early stage of a drug 
286  product development program, especially for drug products with dissolution as the rate-limiting 
  9  Contains Nonbinding Recommendations 
Draft  Not for Implementation 
287  step for absorption, such as MR drug products and IR drug products containing poorly soluble 
288  APIs.  
289   
290  With the mechanistic platform to delineate the complex mechanisms underlying drug absorption, 
291  PBPK modeling for biopharmaceutics applications can provide an estimation of a drug products 
292  in vivo dissolution profile, based on the simulation of in vivo process of drug absorption in the 
293  GI tract. Although the estimated in vivo dissolution profile may be used as a reference, 
294  understanding of the physicochemical properties of the API and drug product quality attributes, 
295  as well as their potential effect on in vivo dissolution, is critical for the development of a 
296  biopredictive/clinically relevant dissolution method. The critical physicochemical properties of 
297  the API and drug product quality attributes include, but are not limited to: (1) its solubility in 
298  aqueous media within physiological pH range (e.g., 1 to 6.8) and/or biorelevant media (e.g., 
299  simulated gastric and intestinal fluid mimicking fasted or fed conditions); (2) saturation or 
300  supersaturation and precipitation properties; (3) mechanism of release; and (4) the in vitro 
301  dissolution characteristics in media at different pH within the physiological range.  
302   
303  By exploring dissolution methodologies (e.g., medium, apparatus, and hydrodynamics), an in 
304  vitro dissolution method can be developed with the intention to predict the in vivo dissolution 
305  profile of a drug product. The use of biorelevant dissolution methodology is encouraged as a 
306  starting point in the development of a biopredictive dissolution method.   
307   
308  To evaluate whether a dissolution method is biopredictive, sponsors should incorporate 
309  dissolution profiles generated by such method into the PBPK model and the predicted systemic 
310  exposure should be comparable (10 percent) to the observed in vivo PK data. To evaluate the 
311  method, we recommend that sponsors use observed in vivo PK data of formulations with 
312  different release rates.  
313   
314  Implementation of a biopredictive method is encouraged for establishing a quality control (QC) 
315  dissolution test. If a biopredictive method is determined inappropriate to be employed for routine 
316  use (e.g., due to method complexity), an alternative dissolution method can be selected as the 
317  primary method for the QC dissolution test while the biopredictive dissolution method can be 
318  retained as an alternate testing approach for aiding in quality assessment when needed. For 
319  instance, dissolution data from a biopredictive method can supersede the primary QC dissolution 
320  testing results to support chemistry, manufacturing, and controls (CMC) changes in terms of 
321  maintaining desired in vivo performance.  
322   
323  2.  Support Clinically Relevant Dissolution Acceptance Criteria 
324   
325  The term clinically relevant dissolution acceptance criteria is defined as a metric that can 
326  identify and reject drug product batches that are not bioequivalent to the pivotal clinical drug 
327  product (Abend et al. 2018). It often refers to the acceptance criteria set for a dissolution method 
328  to minimize the possibility of releasing batches that would have clinical performance differences. 
329  A clinically relevant dissolution acceptance criterion can be wider than that set based on the 
  10  Contains Nonbinding Recommendations 
Draft  Not for Implementation 
330  average dissolution data of pivotal clinical batches (e.g., beyond plus or minus 10 percent 
331  variation range for an extended-release (ER) drug product).13  
332   
333  PBPK modeling for biopharmaceutics applications links in vitro dissolution to PK performance 
334  and hence supports the establishment of clinically relevant dissolution acceptance criteria. PK 
335  predictions from PBPK models for biopharmaceutics applications (e.g., virtual BE studies) based 
336  on in vitro dissolution profile(s) representing the desired limits and/or range of dissolution rate 
337  can be used to justify the clinical relevance of proposed acceptance criteria. The approach should 
338  consider comparing PK predictions based on in vitro dissolution profile(s) representing the 
339  desired dissolution limits and PK predictions based on dissolution profile of pivotal clinical 
340  batches (as a reference). Sponsors should consider the following when conducting virtual BE 
341  studies: (1) the estimated intra- and intersubject variability for PK parameters (such as C  and 
max
342  AUC) should be representative of the observed intra- and intersubject variability; (2) the number 
343  of subjects for virtual BE trials should be justified and comparable to in vivo BE studies; and (3) 
344  the number of virtual BE trials used to estimate the probability of concluding BE should be 
345  justified. 
346   
347  If the outcome of the analysis meets BE acceptance criteria, the proposed dissolution acceptance 
348  criterion could support clinical relevance. For ER drug products, clinically relevant dissolution 
349  acceptance criteria preferably should be set such that all lots/batches that have dissolution 
350  profiles within the upper and lower limits of the specification are bioequivalent to one another. 
351  Less optimally, lots/batches exhibiting dissolution profiles at the upper and lower dissolution 
352  limits should be bioequivalent to the clinical/BA lots/batches or to an appropriate reference 
353  standard, but not necessarily to one another.14 For generic drug products, the predicted PK 
354  performance corresponding to the upper and lower limits of dissolution should support that 
355  product variants are bioequivalent to each other and to the reference listed drug.   
356   
357  Parameter sensitivity analysis (PSA) also can be performed to evaluate the effect of the 
358  dissolution rate change on systemic exposure using validated PBPK models for 
359  biopharmaceutics applications, in support of the clinical relevance of proposed dissolution 
360  acceptance criteria.  
361   
362  B.  Establishment of Clinically Relevant Drug Product Quality Specifications 
363  (Other Than Dissolution) 
364   
365  QbD is a systematic approach for pharmaceutical development and manufacturing to enhance 
366  drug product quality with more consideration of the drug products intended use by the patients; 
367  nevertheless, it is often challenging to establish clinically relevant specifications for drug 
368  substances, excipients, in-process materials, and finished drug products. Current quality testing 
369  or control is largely based on in vitro testing/performance (including in vitro dissolution) of 
370  clinical, development, and registration batches. Although clinical data alone are often insufficient 
371  to inform appropriate drug product specifications, the overall clinical pharmacology and 
                                                 
13 See the guidance for industry Extended Release Oral Dosage Forms: Development, Evaluation, and Application 
of In Vitro/In Vivo Correlations. 
 
14 See the guidance for industry Dissolution Testing of Immediate Release Solid Oral Dosage Forms. 
  11  Contains Nonbinding Recommendations 
Draft  Not for Implementation 
372  biopharmaceutics information gathered during drug product development can be leveraged for 
373  the development of PBPK models for biopharmaceutics applications. These models may help in 
374  establishing a desired in vitro-in vivo link, a key element in building clinical relevance for drug 
375  product quality attributes. 
376   
377  Provided that the quality attributes and process parameters are incorporated either directly or 
378  indirectly in the model, the effect of these attributes and parameters on in vivo dissolution and 
379  absorption can be assessed. The quality attributes can include: (1) drug substance quality 
380  attributes (e.g., particle size distribution, physical form, polymorphic form); (2) excipient quality 
381  attributes (e.g., type and/or level of release rate controlling excipient); (3) in-process quality 
382  attributes (e.g., granule particle size, coating weight gain); and (4) finished drug product 
383  attributes (e.g., disintegration). Manufacturing process parameters include, but are not limited to, 
384  coating parameters and compression force. A biopredictive dissolution profile can be used to 
385  assess the in vivo effect of the quality attributes and process parameters that cannot be directly 
386  input into the model.  
387   
388  Similar to setting clinically relevant dissolution acceptance criteria, clinically relevant drug 
389  product specifications for quality attributes other than dissolution can be established based on 
390  modeling predictions to ensure BE of batches within the specification limits to the pivotal 
391  clinical/BA batches (see section V.A.2., Support Clinically Relevant Dissolution Acceptance 
392  Criteria) or to the reference listed drug for generic drugs.  
393   
394  C.  Quality Risk Assessment for Pre- and Postapproval Changes and Risk-Based 
395  Biowaivers 
396   
397  Sponsors can employ PBPK analyses for biopharmaceutics applications as an advanced tool for 
398  quality risk assessment and management in pharmaceutical development and drug product life 
399  cycle. Specifically, enhanced understanding can be provided by the modeling approach on how 
400  quality attributes affect clinical performance, thereby aiding in risk assessment as part of 
401  formulation and process development and the establishment of the control strategy, as well as 
402  supporting postapproval changes.  
403   
404  The use of PBPK analyses for biopharmaceutics applications at pre- and postapproval stages can 
405  include: 
406   
407    Preapproval Stage 
408   
409    Establishing clinically relevant manufacturing design space and control strategy to 
410  mitigate quality risks in support of patient-centric drug product development 
411   
412    Bridging clinical batches to the to-be-marketed commercial product accounting for 
413  the CMC changes such as formulation, manufacturing process, and manufacturing 
414  site changes made during pharmaceutical development 
415   
  12  Contains Nonbinding Recommendations 
Draft  Not for Implementation 
416    Post-approval Stage 
417   
418    Conducting risk assessment/risk classification as per SUPAC and/or the draft ICH 
419  guidances for industry Q12 Technical and Regulatory Considerations for 
420  Pharmaceutical Product Lifecycle Management: Core Guideline (November 2017) 
421  and Q12 Technical and Regulatory Considerations for Pharmaceutical Product 
422  Lifecycle Management: Annex (November 2017)15 on postapproval CMC changes 
423  such as formulation, manufacturing process, and manufacturing site changes  
424   
425    Supporting biowaivers for postapproval changes 
426   
427  Risk assessment can be performed using the same approach as illustrated in section V.A. and B. 
428  in setting clinically relevant drug product specifications. In this regard, model prediction(s) or 
429  PSA results may be used to support high-impact CMC changes that may otherwise need an in 
430  vivo BE study per the guidances for industry Immediate Release Solid Oral Dosage Forms 
431  Scale-Up and Postapproval Changes: Chemistry, Manufacturing, and Controls, In Vitro 
432  Dissolution Testing, and In Vivo Bioequivalence Documentation and SUPAC-MR: Modified 
433  Release Solid Oral Dosage Forms Scale-Up and Postapproval Changes: Chemistry, 
434  Manufacturing, and Controls; In Vitro Dissolution Testing and In Vivo Bioequivalence 
435  Documentation (September 1997). FDA may grant a biowaiver request supported by PBPK 
436  modeling for biopharmaceutics applications after evaluation of the outcome of the risk 
437  assessment, the level of impact of the proposed change, and the totality of the provided 
438  information.  
439   
                                                 
15 When final, these guidances will represent the FDAs current thinking on these topics.   
  13  Contains Nonbinding Recommendations 
Draft  Not for Implementation 
440  GLOSSARY 
441   
442  Biopharmaceutics: The study of the physical and chemical properties of a drug, its dosage form, 
443  and formulation, as related to the onset, duration, and intensity of drug action. 
444    
445  Biopredictive dissolution method: A set of testing conditions for which in vitro dissolution 
446  profiles are capable of predicting PK profiles. These are typically based on classical or 
447  mechanistic IVIVC. 
448   
449  Biorelevant dissolution method: A set of testing conditions (e.g., media and hydrodynamics) 
450  for monitoring in vitro dissolution designed to closely mimic a relevant biological fluid and a 
451  physiological environment. 
452   
453  Clinically relevant dissolution specification: A specification that takes into consideration the 
454  clinical effect of variations in dissolution ensuring a consistent safety and efficacy profile.  
455   
456  Critical process parameter (CPP): A process parameter whose variability has an effect on a 
457  CQA and therefore should be monitored or controlled to ensure the process produces the desired 
458  quality.16  
459   
460  Critical quality attribute (CQA): A physical, chemical, biological, or microbiological property 
461  or characteristic that should be within an appropriate limit, range, or distribution to ensure the 
462  desired drug product quality.17 CQAs are generally associated with the drug substance, 
463  excipients, intermediates (in-process materials), and drug product.  
464   
465  Design space: The multidimensional combination and interaction of input variables (e.g., 
466  material attributes) and process parameters that have been demonstrated to ensure quality. 
467  Working within the design space is not considered to be a change. Movement out of the design 
468  space is considered to be a change and would normally initiate a regulatory postapproval change 
469  process. Design space is proposed by the sponsor and is subject to regulatory assessment and 
470  approval.18  
471   
472  In vitro/in vivo correlation (IVIVC): A predictive mathematical model describing the 
473  relationship between an in vitro property of an ER dosage form (usually the rate or extent of drug 
474  dissolution or release) and a relevant in vivo response (e.g., plasma drug concentration or amount 
475  of drug absorbed).19  
476   
477  In vitro-in vivo relationship: A qualitative rank-order relationship between a relevant in vivo 
478  response and in vitro release profiles.  
                                                 
16 See ICH Q8(R2). 
 
17 Ibid. 
 
18 Ibid. 
 
19 See the guidance for industry Extended Release Oral Dosage Forms: Development, Evaluation, and Application 
of In Vitro/In Vivo Correlations. 
 
  14  Contains Nonbinding Recommendations 
Draft  Not for Implementation 
479   
480  Parameter sensitivity analysis (PSA): A series of analyses targeting the same estimand, with 
481  differing assumptions to explore the robustness of inferences from the main estimator to 
482  deviations from its underlying modeling assumptions and limitations in the data.20  
483   
484  Physiologically based pharmacokinetic (PBPK) analysis: An analysis using models and 
485  simulations that combine physiology, population, and drug characteristics to mechanistically 
486  describe the PK and/or pharmacodynamic behaviors of a drug product.21  
487   
488  Risk assessment: A systematic process of organizing information to support a risk decision to be 
489  made within a risk management process. It consists of the identification of hazards and the 
490  analysis and evaluation of risks associated with exposure to those hazards.22  
491   
492  Safe space: Boundaries defined by in vitro specifications, such as dissolution or other relevant 
493  drug product quality attributes, within which drug product variants are anticipated to be 
494  bioequivalent to one another. 
                                                 
20 See the draft ICH guidance for industry E9(R1) Statistical Principles for Clinical Trials: Addendum: Estimands 
and Sensitivity Analysis in Clinical Trials (June 2017). When final, this guidance will represent the FDAs current 
thinking on this topic. 
 
21 See the guidance for industry Physiologically Based Pharmacokinetic Analyses  Format and Content. 
 
22 See the ICH guidance for industry Q9 Quality Risk Management (June 2006). 
  15  Contains Nonbinding Recommendations 
Draft  Not for Implementation 
495  REFERENCES 
496   
497  Abend A, Heimbach T, Cohen M, Kesisoglou F, Pepin X, and Suarez-Sharp S, 2018, Dissolution 
498  and Translational Modeling Strategies Enabling Patient-Centric Drug Product Development: The 
499  M-CERSI Workshop Summary Report, AAPS J, 20:60. 
500   
501  Babiskin A and Zhang X, 2015, Application of Physiologically Based Absorption Modeling for 
502  Amphetamine Salts Drug Products in Generic Drug Evaluation, J Pharm Sci, Sep;104(9):3170
503  3182. 
504   
505  Heimbach T, Suarez-Sharp S, Kakhi M, Holmstock N, Olivares-Morales A, Pepin X, Sjogren E, 
506  Tsakalozou E, Seo P, Li M, Zhang X, Lin HP, Montague T, Mitra A, Morris D, Patel N, and 
507  Kesisoglou F, 2019, Dissolution and Translational Modeling Strategies Toward Establishing an 
508  In Vitro-In Vivo Link  A Workshop Summary Report, AAPS J, Feb 11;21(2):29. 
509   
510  Huang SM, Abernethy DR, Wang Y, Zhao P, and Zineh I, 2013, The Utility of Modeling and 
511  Simulation in Drug Development and Regulatory Review, J Pharm Sci, 102(9):29122923. 
512   
513  Kesisoglou F, Chung J, Van Asperen J, and Heimbach T, 2016, Physiologically Based 
514  Absorption Modeling to Impact Biopharmaceutics and Formulation Strategies in Drug 
515  Development-Industry Case Studies, J Pharm Sci, Sep;105(9):27232734. 
516   
517  Kostewicz ES, Aarons L, Bergstrand M, Bolger MB, Galetin A, Hatley O, Jamei M, Lloyd R, 
518  Pepin X, Rostami-Hodjegan A, Sjogren E, Tannergren C, Turner DB, Wagner C, Weitschies W, 
519  and Dressman J, 2014a, PBPK Models for the Prediction of In Vivo Performance of Oral Dosage 
520  Forms, Eur. J. Pharm. Sci., 57:300321. 
521   
522  Kostewicz ES, Abrahamsson B, Brewster M, Brouwers J, Butler J, Carlert S, Dickinson PA, 
523  Dressman J, Holm R, Klein S, Mann J, McAllister M, Minekus M, Muenster U, Mullertz A, 
524  Verwei M, Vertzoni M, Weitschies W, and Augustijns P, 2014b, In Vitro Models for the 
525  Prediction of In Vivo Performance of Oral Dosage Forms, Eur J Pharm Sci, Jun 16;57:342366.  
526   
527  Kuemmel C, Yang Y, Zhang X, Florian J, Zhu H, Tegenge M, Huang SM, Wang Y, Morrison T, 
528  and Zineh I, 2020, Consideration of a Credibility Assessment Framework in Model-Informed 
529  Drug Development: Potential Application to Physiologically-Based Pharmacokinetic Modeling 
530  and Simulation, CPT Pharmacometrics Syst Pharmacol, Jan;9(1):2128. 
531   
532  Nguyen MA, Flanagan T, Brewster M, Kesisoglou F, Beato S, Biewenga J, Crison J, Holm R, Li 
533  R, Mannaert E, McAllister M, Mueller-Zsigmondy M, Muenster U, Ojala K, Page S, Parr A, 
534  Rossenu S, Timmins P, Van Peer A, Vermeulen A, and Langguth P, 2017, A Survey on 
535  IVIVC/IVIVR Development in the Pharmaceutical Industry  Past Experience and Current 
536  Perspectives, Eur J Pharm Sci, May 1;102:113. 
537   
538  Suarez-Sharp S, Cohen M, Kesisoglou F, Abend A, Marroum P, Delvadia P, Kotzagiorgis E, Li 
539  M, Nordmark A, Bandi N, Sjogren E, Babiskin A, Heimbach T, Kijima S, Mandula H, Raines K, 
  16  Contains Nonbinding Recommendations 
Draft  Not for Implementation 
540  Seo P, and Zhang X, 2018, Applications of Clinically Relevant Dissolution Testing: Workshop 
541  Summary Report, AAPS J, Aug 27;20(6):93. 
542   
543  Suarez-Sharp S, Li M, Duan J, Shah H, and Seo P, 2016, Regulatory Experience With In Vivo In 
544  Vitro Correlations (IVIVC) in New Drug Applications, AAPS J, Nov;18(6):13791390. 
545   
546  Wagner C, Pan Y, Hsu V, Grillo JA, Zhang L, Reynolds KS, Sinha V, and Zhao P, 2015a, 
547  Predicting the Effect of Cytochrome P450 Inhibitors on Substrate Drugs: Analysis of 
548  Physiologically Based Pharmacokinetic Modeling Submissions to the US Food and Drug 
549  Administration, Clin Pharmacokinet, 54(1):117127. 
550   
551  Wagner C, Pan Y, Hsu V, Sinha V, and Zhao P, 2016, Predicting the Effect of CYP3A Inducers 
552  on the Pharmacokinetics of Substrate Drugs Using Physiologically Based Pharmacokinetic 
553  (PBPK) Modeling: An Analysis of PBPK Submissions to the US FDA, Clin Pharmacokinet, 
554  55(4):475483. 
555   
556  Wagner C, Zhao P, Pan Y, Hsu V, Grillo J, Huang SM, and Sinha V, 2015b, Application of 
557  Physiologically Based Pharmacokinetic (PBPK) Modeling to Support Dose Selection: Report of 
558  an FDA Public Workshop on PBPK, CPT Pharmacometrics Syst Pharmacol, 4(4):226230. 
559   
560  Zhang X, Duan J, Kesisoglou F, Novakovic J, Amidon GL, Jamei M, Lukacova V, Eissing T, 
561  Tsakalozou E, Zhao L, and Lionberger R, 2017, Mechanistic Oral Absorption Modeling and 
562  Simulation for Formulation Development and Bioequivalence Evaluation: Report of an FDA 
563  Public Workshop, CPT Pharmacometrics Syst. Pharmacol, 6:492495. 
564   
565  Zhang X, Lionberger RA, Davit BM, and Yu LX, 2011, Utility of Physiologically Based 
566  Absorption Modeling in Implementing Quality by Design in Drug Development, AAPS J, 
567  Mar;13(1):5971. 
568   
  17