perspec tives of remote and real-time health care. The scale and power of “big data” analytics should foster greater storage and deeper real-time interpretation of medical information, bringing more possibilities into the mix. We are just at the beginning of this era, and, despite the current profusion of medical apps, both useful and not, more and more useful apps will appear tomorrow to hopefully improve human health while reducing the inefficiency and costs of the health-care system that supports us. CONFLICT OF INTEREST The author is a member of the boards of directors
of Illumina, Aveo, Fate Therapeutics, and Proteus Digital Health. © 2014 ASCPT
1. Balas, E. & Boren, S. Managing clinical knowledge for health care improvement. In Yearbook of Medical Informatics (eds. van Bemmel, J.H. & McCray, A.T.) 65–70 (Schattauer Verlagsgesellschaft, Stuttgart, Germany, 2000). 2. Derbyshire, D. & Dancey, D. Smartphone medical applications for women’s health: what is the evidence-base and feedback? Int. J. Telemed. Appl. 2013, 782074 (2013). 3. Eysenbach, G.; CONSORT-EHEALTH Group. CONSORT-EHEALTH: improving and standardizing evaluation reports of Webbased and mobile health interventions. J. Med. Internet Res.13, e126 (2011).
See article page 550
Physiologically Based Pharmacokinetic Modeling: From Regulatory Science to Regulatory Policy V Sinha1, P Zhao1, SM Huang1 and I Zineh1 Assessment of controllable sources of intra- and interpatient variability in drug response is of critical importance in the regulatory evaluation of new drugs.1 Although determinants of response variability would ideally be understood and accounted for before approval of a new pharmaceutical product, this is rarely the case for all; clinical trials in specific populations that definitively test optimal dosing in patient management strategies are not routinely performed prior to drug approval. Current status
Ethical and practical issues may limit the numbers of studies one can conduct to test all clinically relevant scenarios, a situation that has resulted in the need for innovation in drug development and evaluation to address these knowledge gaps and construct recommendations for
safe and effective drug use. The US Food and Drug Administration (FDA) has communicated the need for innovation in clinical evaluation to enhance medicalproduct development as part of its strategic plan for regulatory science.2 Modeling and simulation are among the enabling approaches to accomplish the envisioned
1Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA. Correspondence: V Sinha (vikram.sinha@fda. hhs.gov)
doi:10.1038/clpt.2014.46 478
efficiency and effectiveness in drug development. The FDA Office of Clinical Pharmacology (OCP) has recently described its efforts in development of model-based regulatory science and resultant application to regulatory review of therapeutic products. One such area of active development is physiologically based pharmacokinetic (PBPK) modeling. As a model using system- and drug-specific information, PBPK is increasingly being applied during drug discovery and development, and is informing regulatory review including drug labeling (Table 1).3–5 In 2013, approximately 50 submissions either requested scientific advice on using a PBPK approach or included such an approach in approval filings. This is a reflection of significant advances in utilization of in vitro data, greater understanding of human physiology, and advancement in database and software development. The OCP experience to date suggests that PBPK may be useful in (i) planning and assessing conventional and population PK trial designs; (ii) predicting PK as a result of intrinsic and/or extrinsic factors and assessing the impact of sources of variability for untested clinical scenarios; and (iii) evaluating or confirming dosing recommendations in specific populations. From a clinical pharmacology standpoint, PBPK has indeed been broadly applied, with varying degrees of accompanying regulatory experience (Table 1).4,5 What follows are high-level synopses of our experience to date with PBPK in several areas. Drug–drug and gene–drug interactions. As previously described, drugs that are susceptible to pharmacokinetic drug–drug interactions (DDIs) are often likewise susceptible to high interindividual variability due to genetic differences in the population (e.g., in drug-metabolizing enzyme genes).6 In fact, DDIs and gene–drug interactions are arguably two of the major sources of variability in drug disposition, and they often coexist in a patient. The OCP’s most extensive experience is in the evaluation of an investigational drug’s potential for cytochrome P450 (CYP)-mediated DDIs in various clinical VOLUME 95 NUMBER 5 | MAY 2014 | www.nature.com/cpt
perspec tives Table 1 Examples of PBPK-informed drug labeling Specific question(s) addressed using PBPK
Links to reviews and labels
Sildenafil injection
Effect of a strong CYP3A inhibitor on intravenous sildenafil exposure (vs. oral sildenafil)
Review: http://www.accessdata.fda.gov/drugsatfda_docs/nda/2009/022473s000_ ClinPharmR.pdf Label: http://www.accessdata.fda.gov/drugsatfda_docs/label/2012/022473s003lbl.pdf
Diltiazem
Interaction of diltiazem with simvastatin
Review: http://www.accessdata.fda.gov/drugsatfda_docs/label/2010/021392s014lbl.pdf Label: http://www.accessdata.fda.gov/drugsatfda_docs/label/2012/019766s087s088lbl. pdf; see also the label for simvastatin
Ponatinib
Effect of a strong CYP3A inducer (rifampin) Review: http://www.accessdata.fda.gov/drugsatfda_docs/nda/2012/203469Orig1s000C on ponatinib exposure linPharmR.pdf Label: http://www.accessdata.fda.gov/drugsatfda_docs/label/2012/203469lbl.pdf
Rivaroxaban
Assessing a complex and multiple interaction scenario: subjects with renal impairment and coadministered a combined P-gp and CYP3A4 inhibitor (weak or moderate)
Review: http://www.accessdata.fda.gov/drugsatfda_docs/nda/2011/022406Orig1s000C linPharmR.pdf Label: http://www.accessdata.fda.gov/drugsatfda_docs/label/2014/022406s007lbl.pdf
Macitentan
Effect of a strong CYP3A inhibitor on macitentan steady-state exposure
Review: http://www.accessdata.fda.gov/drugsatfda_docs/nda/2013/204410Orig1s000C linPharmR.pdf Label: http://www.accessdata.fda.gov/drugsatfda_docs/label/2013/204410s000lbl. pdfRefs. 19, 20
Ibrutinib
Effect of a moderate CYP3A inducer or inhibitor on ibrutinib exposure
Review: http://www.accessdata.fda.gov/drugsatfda_docs/nda/2013/205552Orig1s000C linPharmR.pdf Label: http://www.accessdata.fda.gov/drugsatfda_docs/label/2013/205552s000lbl.pdf
Simeprevir
Assessing the significance of a transporter Review: http://www.accessdata.fda.gov/drugsatfda_docs/nda/2013/205123Orig1s000C (OATP1B1/3) on simeprevir disposition linPharmR.pdf Label: http://www.accessdata.fda.gov/drugsatfda_docs/label/2013/205123s001lbl.pdf
Drug
For detailed information about specific drug products and a list of drug approval reports by month, search the FDA-Approved Drug Products database (http://www.accessdata. fda.gov/scripts/cder/drugsatfda). CYP3A, cytochrome P450 3A; PBPK, pharmacologically based pharmacokinetics; P-gp, P-glycoprotein.
scenarios. Our more modest familiarity with transporter-mediated DDIs is due primarily to uncertainty in system parameters pertinent to our understanding of transporters. Factors influencing the in vitro–in vivo extrapolation of transporter experimental data, such as the expression level of transporters in different tissues, are being studied.7 To the extent that the functional consequences of genetic polymorphisms in drug-metabolizing enzymes and their frequencies across various ethnic groups are well documented, this information has been used to evaluate their impact on drug exposure through the use of simulations that allow stochastic modeling of virtual populations.8 In some situations, a multistep approach may be appropriate when planning to use PBPK to determine the probable effects of DDIs and/or gene–drug interactions on drug PK and subsequent need for dedicated studies, as has been recently described for four drugs that are eliminated by both CYP3A and CYP2D6 (ref. 9). Specifically, development of a PBPK model can be based on in vitro metabolism
and initial human PK data. The approach starts with basic models and moves on to static mechanistic models, followed by dynamic mechanistic models. Simulation exercises can then be performed and refined with additional data as needed. Labeling, management strategies, and need for additional studies could then be driven by the magnitude and clinical relevance of the simulated results, contextualized within the model’s robustness to changing assumptions. These exercises can be superimposed on key drug development milestones and regulator interfaces ranging from the end of phase I to the end of phase II junctures. Specific populations. PBPK modeling can be an attractive complement to the conduct and interpretation of studies in patients with organ impairment (e.g., hepatic or renal), a logistically and clinically challenging population to study. In this context, however, the applicability of PBPK is limited by the relatively underdeveloped body of information on the impact of organ impairment on
important model parameters. We have found that the predictive performance of PBPK models of hepatic impairment has not been sufficiently developed. Furthermore, due consideration of the combined effects of advancing age, body habitus, and organ impairment is necessary when constructing PBPK models to assess drug effects in the elderly. PBPK modeling may also have utility in predicting PK differences in pediatric patients relative to adults. PBPK may be particularly useful in pediatric populations for which allometric scaling or other standard modeling methods are not sufficiently informative. We have found that standard modeling approaches (e.g., allometry) generally work well in patients more than 2 years old10 and that PBPK may have greater utility in younger patients when key differences in maturation of multiple physiological processes need to be accounted for. Future directions
A major area of policy development within the FDA OCP is how PBPK can be
Clinical pharmacology & Therapeutics | VOLUME 95 NUMBER 5 | MAY 2014
479
perspec tives used within a broader framework to help decide whether, when, and how to conduct a dedicated clinical pharmacology study intended to address optimal dosing in a specific population. Furthermore, best practices are needed to appropriately utilize PBPK modeling to forecast altered PK in situations that have not been clinically evaluated (e.g., complex drug interactions, multiple comorbidities). Because PBPK models consist of three distinct interrelated components—system-specific properties, drug properties, and a structural model—assessment of methodology and fidelity can be challenging. Important features of any PBPK-based platform should include the verifiable source of a large amount of physiological data including parameter estimates. Therefore, key methodological, analytical, and interpretive issues must be addressed in order to (i) determine the exact value proposition of PBPK in drug development and evaluation and (ii) address the issues necessary to ensure the predictable use of PBPK modeling to inform decisions regarding study waivers and prescription drug labeling. While the science of PBPK evolves, the FDA will continue to promulgate its current recommendations in updated clinical pharmacology guidance documents, when appropriate. We have, however, identified a need for scientific engagement in order to refine our thinking in key areas of model-informed drug development. In addition to public workshops held to address several of these matters, we seek early engagement with drug developers who intend to use PBPK and other integrative approaches to maximize learning from early-phase clinical trials. Although the FDA review experience is steadily increasing and the challenges in using PBPK remain great, we expect that applications of PBPK will continue to show utility during drug development and regulatory review. To achieve its full potential, open and focused collaborations among government agencies, industry, and academia are critical in further developing these models. CONFLICT OF INTEREST All of the authors are FDA employees. S.M.H. is an Associate Editor of CPT but was not involved in the review or decision process for this article. 480
© 2014 ASCPT
1. Peck, C.C., Temple, R. & Collins, J.M. Understanding consequences of concurrent therapies. JAMA 269, 1550–1552 (1993). 2. US Food and Drug Administration. Advancing Regulatory Science for Public Health—A Framework for FDA’s Regulatory Science Initiative (October 2010). 3. Huang, S.M., Abernethy, D.R., Wang, Y., Zhao, P. & Zineh, I. The utility of modeling and simulation in drug development and regulatory review. J. Pharm. Sci. 102, 2912–2923 (2013). 4. Rowland, M., Peck, C. & Tucker, G. Physiologicallybased pharmacokinetics in drug development and regulatory science. Annu. Rev. Pharmacol. Toxicol. 51, 45–73 (2011). 5. Zhao, P. et al. Applications of physiologically based pharmacokinetic (PBPK) modeling and simulation during regulatory review. Clin. Pharmacol. Ther. 89, 259–267 (2011). 6. Conrado, D.J., Rogers, H.L., Zineh, I. & Pacanowski,
7. 8.
9.
10.
M.A. Consistency of drug–drug and gene–drug interaction information in US FDA-approved drug labels. Pharmacogenomics 14, 215–223 (2013). Giacomini, K.M. & Huang, S.M. Transporters in drug development and clinical pharmacology. Clin. Pharmacol. Ther. 94, 3–9 (2013). Rostami-Hodjegan, A. Physiologically based pharmacokinetics joined with in vitro–in vivo extrapolation of ADME: a marriage under the arch of systems pharmacology. Clin. Pharmacol. Ther. 92, 50–61 (2012). Vieira, M.dLT. et al. PBPK model describes the effects of comedication and genetic polymorphism on systemic exposure of drugs that undergo multiple clearance pathways. Clin. Pharmacol. Ther. 95, 550–557 (2014). US Food and Drug Administration. Presentation at 2012 FDA Pharmaceutical Sciences and Clinical Pharmacology Advisory Committee Meeting (14 March 2012).
Modeling and Simulation of Biopharmaceutical Performance X Zhang1 and RA Lionberger1 Biopharmaceutical performance refers to the influence of pharmaceutical formulation variables on in vivo performance. New drug product success depends on formulation design for sufficient bioavailability for clinically desired dosing. Regulatory interest in biopharmaceutical performance includes batch-to-batch consistency, acceptability of postapproval changes, and evaluation of bioequivalence (BE) for generic drug products. This Commentary summarizes biopharmaceutical modeling and simulation in the US Food and Drug Administration (FDA) Office of Generic Drugs (OGD) for orally administered generic drugs. Modeling and simulation of absorption
The purpose of oral-absorption modeling and simulation is to predict bioavailability, which is the product of Fa (the fraction of drug absorption from the gastrointestinal (GI) tract), Fg (the fraction of drug that escapes the gut
extraction), and Fh (the fraction of drug that escapes the liver extraction). Figure 1 is a schematic that briefly describes the absorption process and factors that affect oral absorption. In the whole process, GI physiological parameters all interact with the drug substance and product to determine bioavailability.
1Office of Generic Drugs, US Food and Drug Administration, Rockville, Maryland, USA. Correspondence: RA Lionberger ([email protected])
doi:10.1038/clpt.2014.40 VOLUME 95 NUMBER 5 | MAY 2014 | www.nature.com/cpt
of Illumina, Aveo, Fate Therapeutics, and Proteus Digital Health. © 2014 ASCPT
1. Balas, E. & Boren, S. Managing clinical knowledge for health care improvement. In Yearbook of Medical Informatics (eds. van Bemmel, J.H. & McCray, A.T.) 65–70 (Schattauer Verlagsgesellschaft, Stuttgart, Germany, 2000). 2. Derbyshire, D. & Dancey, D. Smartphone medical applications for women’s health: what is the evidence-base and feedback? Int. J. Telemed. Appl. 2013, 782074 (2013). 3. Eysenbach, G.; CONSORT-EHEALTH Group. CONSORT-EHEALTH: improving and standardizing evaluation reports of Webbased and mobile health interventions. J. Med. Internet Res.13, e126 (2011).
See article page 550
Physiologically Based Pharmacokinetic Modeling: From Regulatory Science to Regulatory Policy V Sinha1, P Zhao1, SM Huang1 and I Zineh1 Assessment of controllable sources of intra- and interpatient variability in drug response is of critical importance in the regulatory evaluation of new drugs.1 Although determinants of response variability would ideally be understood and accounted for before approval of a new pharmaceutical product, this is rarely the case for all; clinical trials in specific populations that definitively test optimal dosing in patient management strategies are not routinely performed prior to drug approval. Current status
Ethical and practical issues may limit the numbers of studies one can conduct to test all clinically relevant scenarios, a situation that has resulted in the need for innovation in drug development and evaluation to address these knowledge gaps and construct recommendations for
safe and effective drug use. The US Food and Drug Administration (FDA) has communicated the need for innovation in clinical evaluation to enhance medicalproduct development as part of its strategic plan for regulatory science.2 Modeling and simulation are among the enabling approaches to accomplish the envisioned
1Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA. Correspondence: V Sinha (vikram.sinha@fda. hhs.gov)
doi:10.1038/clpt.2014.46 478
efficiency and effectiveness in drug development. The FDA Office of Clinical Pharmacology (OCP) has recently described its efforts in development of model-based regulatory science and resultant application to regulatory review of therapeutic products. One such area of active development is physiologically based pharmacokinetic (PBPK) modeling. As a model using system- and drug-specific information, PBPK is increasingly being applied during drug discovery and development, and is informing regulatory review including drug labeling (Table 1).3–5 In 2013, approximately 50 submissions either requested scientific advice on using a PBPK approach or included such an approach in approval filings. This is a reflection of significant advances in utilization of in vitro data, greater understanding of human physiology, and advancement in database and software development. The OCP experience to date suggests that PBPK may be useful in (i) planning and assessing conventional and population PK trial designs; (ii) predicting PK as a result of intrinsic and/or extrinsic factors and assessing the impact of sources of variability for untested clinical scenarios; and (iii) evaluating or confirming dosing recommendations in specific populations. From a clinical pharmacology standpoint, PBPK has indeed been broadly applied, with varying degrees of accompanying regulatory experience (Table 1).4,5 What follows are high-level synopses of our experience to date with PBPK in several areas. Drug–drug and gene–drug interactions. As previously described, drugs that are susceptible to pharmacokinetic drug–drug interactions (DDIs) are often likewise susceptible to high interindividual variability due to genetic differences in the population (e.g., in drug-metabolizing enzyme genes).6 In fact, DDIs and gene–drug interactions are arguably two of the major sources of variability in drug disposition, and they often coexist in a patient. The OCP’s most extensive experience is in the evaluation of an investigational drug’s potential for cytochrome P450 (CYP)-mediated DDIs in various clinical VOLUME 95 NUMBER 5 | MAY 2014 | www.nature.com/cpt
perspec tives Table 1 Examples of PBPK-informed drug labeling Specific question(s) addressed using PBPK
Links to reviews and labels
Sildenafil injection
Effect of a strong CYP3A inhibitor on intravenous sildenafil exposure (vs. oral sildenafil)
Review: http://www.accessdata.fda.gov/drugsatfda_docs/nda/2009/022473s000_ ClinPharmR.pdf Label: http://www.accessdata.fda.gov/drugsatfda_docs/label/2012/022473s003lbl.pdf
Diltiazem
Interaction of diltiazem with simvastatin
Review: http://www.accessdata.fda.gov/drugsatfda_docs/label/2010/021392s014lbl.pdf Label: http://www.accessdata.fda.gov/drugsatfda_docs/label/2012/019766s087s088lbl. pdf; see also the label for simvastatin
Ponatinib
Effect of a strong CYP3A inducer (rifampin) Review: http://www.accessdata.fda.gov/drugsatfda_docs/nda/2012/203469Orig1s000C on ponatinib exposure linPharmR.pdf Label: http://www.accessdata.fda.gov/drugsatfda_docs/label/2012/203469lbl.pdf
Rivaroxaban
Assessing a complex and multiple interaction scenario: subjects with renal impairment and coadministered a combined P-gp and CYP3A4 inhibitor (weak or moderate)
Review: http://www.accessdata.fda.gov/drugsatfda_docs/nda/2011/022406Orig1s000C linPharmR.pdf Label: http://www.accessdata.fda.gov/drugsatfda_docs/label/2014/022406s007lbl.pdf
Macitentan
Effect of a strong CYP3A inhibitor on macitentan steady-state exposure
Review: http://www.accessdata.fda.gov/drugsatfda_docs/nda/2013/204410Orig1s000C linPharmR.pdf Label: http://www.accessdata.fda.gov/drugsatfda_docs/label/2013/204410s000lbl. pdfRefs. 19, 20
Ibrutinib
Effect of a moderate CYP3A inducer or inhibitor on ibrutinib exposure
Review: http://www.accessdata.fda.gov/drugsatfda_docs/nda/2013/205552Orig1s000C linPharmR.pdf Label: http://www.accessdata.fda.gov/drugsatfda_docs/label/2013/205552s000lbl.pdf
Simeprevir
Assessing the significance of a transporter Review: http://www.accessdata.fda.gov/drugsatfda_docs/nda/2013/205123Orig1s000C (OATP1B1/3) on simeprevir disposition linPharmR.pdf Label: http://www.accessdata.fda.gov/drugsatfda_docs/label/2013/205123s001lbl.pdf
Drug
For detailed information about specific drug products and a list of drug approval reports by month, search the FDA-Approved Drug Products database (http://www.accessdata. fda.gov/scripts/cder/drugsatfda). CYP3A, cytochrome P450 3A; PBPK, pharmacologically based pharmacokinetics; P-gp, P-glycoprotein.
scenarios. Our more modest familiarity with transporter-mediated DDIs is due primarily to uncertainty in system parameters pertinent to our understanding of transporters. Factors influencing the in vitro–in vivo extrapolation of transporter experimental data, such as the expression level of transporters in different tissues, are being studied.7 To the extent that the functional consequences of genetic polymorphisms in drug-metabolizing enzymes and their frequencies across various ethnic groups are well documented, this information has been used to evaluate their impact on drug exposure through the use of simulations that allow stochastic modeling of virtual populations.8 In some situations, a multistep approach may be appropriate when planning to use PBPK to determine the probable effects of DDIs and/or gene–drug interactions on drug PK and subsequent need for dedicated studies, as has been recently described for four drugs that are eliminated by both CYP3A and CYP2D6 (ref. 9). Specifically, development of a PBPK model can be based on in vitro metabolism
and initial human PK data. The approach starts with basic models and moves on to static mechanistic models, followed by dynamic mechanistic models. Simulation exercises can then be performed and refined with additional data as needed. Labeling, management strategies, and need for additional studies could then be driven by the magnitude and clinical relevance of the simulated results, contextualized within the model’s robustness to changing assumptions. These exercises can be superimposed on key drug development milestones and regulator interfaces ranging from the end of phase I to the end of phase II junctures. Specific populations. PBPK modeling can be an attractive complement to the conduct and interpretation of studies in patients with organ impairment (e.g., hepatic or renal), a logistically and clinically challenging population to study. In this context, however, the applicability of PBPK is limited by the relatively underdeveloped body of information on the impact of organ impairment on
important model parameters. We have found that the predictive performance of PBPK models of hepatic impairment has not been sufficiently developed. Furthermore, due consideration of the combined effects of advancing age, body habitus, and organ impairment is necessary when constructing PBPK models to assess drug effects in the elderly. PBPK modeling may also have utility in predicting PK differences in pediatric patients relative to adults. PBPK may be particularly useful in pediatric populations for which allometric scaling or other standard modeling methods are not sufficiently informative. We have found that standard modeling approaches (e.g., allometry) generally work well in patients more than 2 years old10 and that PBPK may have greater utility in younger patients when key differences in maturation of multiple physiological processes need to be accounted for. Future directions
A major area of policy development within the FDA OCP is how PBPK can be
Clinical pharmacology & Therapeutics | VOLUME 95 NUMBER 5 | MAY 2014
479
perspec tives used within a broader framework to help decide whether, when, and how to conduct a dedicated clinical pharmacology study intended to address optimal dosing in a specific population. Furthermore, best practices are needed to appropriately utilize PBPK modeling to forecast altered PK in situations that have not been clinically evaluated (e.g., complex drug interactions, multiple comorbidities). Because PBPK models consist of three distinct interrelated components—system-specific properties, drug properties, and a structural model—assessment of methodology and fidelity can be challenging. Important features of any PBPK-based platform should include the verifiable source of a large amount of physiological data including parameter estimates. Therefore, key methodological, analytical, and interpretive issues must be addressed in order to (i) determine the exact value proposition of PBPK in drug development and evaluation and (ii) address the issues necessary to ensure the predictable use of PBPK modeling to inform decisions regarding study waivers and prescription drug labeling. While the science of PBPK evolves, the FDA will continue to promulgate its current recommendations in updated clinical pharmacology guidance documents, when appropriate. We have, however, identified a need for scientific engagement in order to refine our thinking in key areas of model-informed drug development. In addition to public workshops held to address several of these matters, we seek early engagement with drug developers who intend to use PBPK and other integrative approaches to maximize learning from early-phase clinical trials. Although the FDA review experience is steadily increasing and the challenges in using PBPK remain great, we expect that applications of PBPK will continue to show utility during drug development and regulatory review. To achieve its full potential, open and focused collaborations among government agencies, industry, and academia are critical in further developing these models. CONFLICT OF INTEREST All of the authors are FDA employees. S.M.H. is an Associate Editor of CPT but was not involved in the review or decision process for this article. 480
© 2014 ASCPT
1. Peck, C.C., Temple, R. & Collins, J.M. Understanding consequences of concurrent therapies. JAMA 269, 1550–1552 (1993). 2. US Food and Drug Administration. Advancing Regulatory Science for Public Health—A Framework for FDA’s Regulatory Science Initiative (October 2010). 3. Huang, S.M., Abernethy, D.R., Wang, Y., Zhao, P. & Zineh, I. The utility of modeling and simulation in drug development and regulatory review. J. Pharm. Sci. 102, 2912–2923 (2013). 4. Rowland, M., Peck, C. & Tucker, G. Physiologicallybased pharmacokinetics in drug development and regulatory science. Annu. Rev. Pharmacol. Toxicol. 51, 45–73 (2011). 5. Zhao, P. et al. Applications of physiologically based pharmacokinetic (PBPK) modeling and simulation during regulatory review. Clin. Pharmacol. Ther. 89, 259–267 (2011). 6. Conrado, D.J., Rogers, H.L., Zineh, I. & Pacanowski,
7. 8.
9.
10.
M.A. Consistency of drug–drug and gene–drug interaction information in US FDA-approved drug labels. Pharmacogenomics 14, 215–223 (2013). Giacomini, K.M. & Huang, S.M. Transporters in drug development and clinical pharmacology. Clin. Pharmacol. Ther. 94, 3–9 (2013). Rostami-Hodjegan, A. Physiologically based pharmacokinetics joined with in vitro–in vivo extrapolation of ADME: a marriage under the arch of systems pharmacology. Clin. Pharmacol. Ther. 92, 50–61 (2012). Vieira, M.dLT. et al. PBPK model describes the effects of comedication and genetic polymorphism on systemic exposure of drugs that undergo multiple clearance pathways. Clin. Pharmacol. Ther. 95, 550–557 (2014). US Food and Drug Administration. Presentation at 2012 FDA Pharmaceutical Sciences and Clinical Pharmacology Advisory Committee Meeting (14 March 2012).
Modeling and Simulation of Biopharmaceutical Performance X Zhang1 and RA Lionberger1 Biopharmaceutical performance refers to the influence of pharmaceutical formulation variables on in vivo performance. New drug product success depends on formulation design for sufficient bioavailability for clinically desired dosing. Regulatory interest in biopharmaceutical performance includes batch-to-batch consistency, acceptability of postapproval changes, and evaluation of bioequivalence (BE) for generic drug products. This Commentary summarizes biopharmaceutical modeling and simulation in the US Food and Drug Administration (FDA) Office of Generic Drugs (OGD) for orally administered generic drugs. Modeling and simulation of absorption
The purpose of oral-absorption modeling and simulation is to predict bioavailability, which is the product of Fa (the fraction of drug absorption from the gastrointestinal (GI) tract), Fg (the fraction of drug that escapes the gut
extraction), and Fh (the fraction of drug that escapes the liver extraction). Figure 1 is a schematic that briefly describes the absorption process and factors that affect oral absorption. In the whole process, GI physiological parameters all interact with the drug substance and product to determine bioavailability.
1Office of Generic Drugs, US Food and Drug Administration, Rockville, Maryland, USA. Correspondence: RA Lionberger ([email protected])
doi:10.1038/clpt.2014.40 VOLUME 95 NUMBER 5 | MAY 2014 | www.nature.com/cpt
