This commentary provides an update on the status of physiologically based pharmacokinetic modeling and simulation at the U.S. Food and Drug Administration's Office of Clinical Pharmacology. Limitations and knowledge gaps in integration of physiologically based pharmacokinetic approach to inform regulatory decision making, as well as the importance of scientific engagement with drug developers who intend to use this approach, are highlighted.

Since 2016, results from physiologically based pharmacokinetic (PBPK) analyses have been routinely found in the clinical pharmacology section of regulatory applications submitted to the US Food and Drug Administration (FDA). In 2018, the Food and Drug Administration's Office of Clinical Pharmacology published a commentary summarizing the application of PBPK modeling in the submissions it received between 2008 and 2017 and its impact on prescribing information. In this commentary, we provide an update on the application of PBPK modeling in submissions received between 2018 and 2019 and highlight a few notable examples.

The FDA approved capmatinib and tepotinib on May 6, 2020, and February 3, 2021, respectively. Capmatinib is indicated for patients with metastatic non–small cell lung cancer (mNSCLC) whose tumors have a mutation leading to mesenchymal–epithelial transition (MET) exon 14 skipping as detected by an FDA-approved test. Tepotinib is indicated for mNSCLC harboring MET exon 14 skipping alterations. The approvals were based on trials GEOMETRY mono-1 (capmatinib) and VISION (tepotinib). In GEOMETRY mono-1, overall response rate (ORR) per Blinded Independent Review Committee (BIRC) was 68% [95% confidence interval (CI), 48–84] with median duration of response (DoR) 12.6 months (95% CI, 5.5–25.3) in 28 treatment-naïve patients and 41% (95% CI: 29, 53) with median DoR 9.7 months (95% CI, 5.5–13) in 69 previously treated patients with NSCLC with mutations leading to MET exon 14 skipping. In VISION, ORR per BIRC was 43% (95% CI: 32, 56) with median DoR 10.8 months (95% CI, 6.9–not estimable) in 69 treatment-naïve patients and 43% (95% CI, 33–55) with median DoR 11.1 months (95% CI, 9.5–18.5) in 83 previously-treated patients with NSCLC harboring MET exon 14 alterations. These are the first two therapies to be FDA approved specifically for patients with metastatic NSCLC with MET exon 14 skipping.

Transporters play an important role in drug absorption, disposition, and drug action. The evaluation of drug transporters requires a comprehensive understanding of transporter biology and pharmacology. Physiologically based pharmacokinetic (PBPK) models may offer an integrative platform to quantitatively evaluate the role of drug transporters and its interplay with other drug disposition processes such as passive drug diffusion and elimination by metabolizing enzymes. To date, PBPK modeling and simulations integrating drug transporters lag behind that for drug-metabolizing enzymes. In addition, predictive performance of PBPK has not been well established for predicting the role of drug transporters in the pharmacokinetics of a drug. To enhance overall predictive performance of transporter-based PBPK models, it is necessary to have a detailed understanding of transporter biology for proper representation in the models and to have a quantitative understanding of the contribution of transporters in the absorption and metabolism of a drug. This article summarizes PBPK-based submissions evaluating the role of drug transporters to the Office of Clinical Pharmacology of the US Food and Drug Administration. Keywordstransporter, drug-drug interaction, physiologically based pharmacokinetic models, predictive performance Drug transporters play a critical role in drug absorption, disposition, and action.1,2 However, recognition of the importance of drug transporters and the implementation of a routine assessment in drug development lag behind the metabolizing enzymes. In addition, interpretation of the clinical significance of transporter data is more difficult than that for metabolizing enzymes for several reasons. First, the biology and pharmacology of certain transporters, especially the emerging transporters, are not fully understood. Second, major drug transporters are ubiquitously distributed in the body. For example, if a drug is tested as a substrate of P-glycoprotein (P-gp) in vitro, one needs to consider the role of P-gp in oral absorption through the intestine, distribution into the central nervous system, and elimination via active section in the liver and kidneys. Third, drug transporters are differentially localized in a variety of polarized cells, including enterocytes, hepatocytes, and proximal tubule cells. Within these cells, some transporters are located on the apical side, others on the basolateral side. These transporters often have distinctive functions (eg, efflux versus uptake) and regulate drug concentrations inside and outside the cells. Fourth, regulation of drug concentrations inside and outside the cells may be mediated simultaneously by transporters and passive diffusion processes. If a Pgp substrate has high passive permeability, it may still be effectively absorbed from intestinal lumen. Finally, transporter activities may affect a drug's contact with drug-metabolizing enzymes, a phenomenon known as enzyme-transporter interplay. Physiologically based pharmacokinetic (PBPK) models comb...

There is a continued predisposition of concurrent use of drugs and botanical products. Consumers often self-administer botanical products without informing their health care providers. The perceived safety of botanical products with lack of knowledge of the interaction potential poses a challenge for providers and both efficacy and safety concerns for patients. Botanical-drug combinations can produce untoward effects when botanical constituents modulate drug metabolizing enzymes and/or transporters impacting the systemic or tissue exposure of concomitant drugs. Examples of pertinent scientific literature evaluating the interaction potential of commonly used botanicals in the US are discussed. Current methodologies that can be applied to advance our efforts in predicting drug interaction liability is presented. This review also highlights the regulatory science viewpoint on botanical-drug interactions and labeling implications.

Remdesivir (RDV) is the first drug approved by the US Food and Drug Administration (FDA) for the treatment of coronavirus disease 2019 (COVID‐19) in certain patients requiring hospitalization. As a nucleoside analogue prodrug, RDV undergoes intracellular multistep activation to form its pharmacologically active species, GS‐443902, which is not detectable in the plasma. A question arises that whether the observed plasma exposure of RDV and its metabolites would correlate with or be informative about the exposure of GS‐443902 in tissues. A whole body physiologically‐based pharmacokinetic (PBPK) modeling and simulation approach was utilized to elucidate the disposition mechanism of RDV and its metabolites in the lungs and liver and explore the relationship between plasma and tissue pharmacokinetics (PK) of RDV and its metabolites in healthy subjects. In addition, the potential alteration of plasma and tissue PK of RDV and its metabolites in patients with organ dysfunction was explored. Our simulation results indicated that intracellular exposure of GS‐443902 was decreased in the liver and increased in the lungs in subjects with hepatic impairment relative to the subjects with normal liver function. In subjects with severe renal impairment, the exposure of GS‐443902 in the liver was slightly increased, whereas the lung exposure of GS‐443902 was not impacted. These predictions along with the organ impairment study results may be used to support decision making regarding the RDV dosage adjustment in these patient subgroups. The modeling exercise illustrated the potential of whole body PBPK modeling to aid in decision making for nucleotide analogue prodrugs, particularly when the active metabolite exposure in the target tissues is not available.

Background: Alterations in plasma protein concentrations in pregnant and postpartum individuals can influence antiretroviral (ARV) pharmacokinetics. Physiologically-based pharmacokinetic (PBPK) models can serve to inform drug dosing decisions in understudied populations. However, development of such models requires quantitative physiological information (e.g., changes in plasma protein concentration) from the population of interest.Objective: To quantitatively describe the time-course of albumin and α1-acid glycoprotein (AAG) concentrations in pregnant and postpartum women living with HIV.Methods: Serum and plasma protein concentrations procured from the International Maternal Pediatric Adolescent AIDS Clinical Trial Protocol 1026s (P1026s) were analyzed using a generalized additive modeling approach. Separate non-parametric smoothing splines were fit to albumin and AAG concentrations as functions of gestational age or postpartum duration.Results: The analysis included 871 and 757 serum albumin concentrations collected from 380 pregnant (~20 to 42 wks gestation) and 354 postpartum (0 to 46 wks postpartum) women, respectively. Thirty-six and 32 plasma AAG concentrations from 31 pregnant (~24 to 38 wks gestation) and 30 postpartum women (~2–13 wks postpartum), respectively, were available for analysis. Estimated mean albumin concentrations remained stable from 20 wks gestation to term (33.4 to 34.3 g/L); whereas, concentrations rapidly increased postpartum until stabilizing at ~42.3 g/L 15 wk after delivery. Estimated AAG concentrations slightly decreased from 24 wks gestation to term (53.6 and 44.9 mg/dL) while postpartum levels were elevated at two wks after delivery (126.1 mg/dL) and subsequently declined thereafter. Computational functions were developed to quantitatively communicate study results in a form that can be readily utilized for PBPK model development.Conclusion: By characterizing the trajectory of plasma protein concentrations in pregnant and postpartum women living with HIV, our analysis can increase confidence in PBPK model predictions for HIV antiretrovirals and better inform drug dosing decisions in this understudied population.

A critical step to evaluate the potential in vivo antiviral activity of a drug is to connect the in vivo exposure to its in vitro antiviral activity. The 'Anti-SARS-CoV-2 Repurposing Drug Database' is a Accepted ArticleThis article is protected by copyright. All rights reserved database that includes both in vitro anti-SARS-CoV-2 activity and in vivo pharmacokinetic data to facilitate the extrapolation from in vitro antiviral activity to potential in vivo antiviral activity for a large set of drugs/compounds. In addition to serving as a data source for in vitro anti-SARS-CoV-2 activity and in vivo pharmacokinetic information, the database is also a calculation tool that can be used to compare the in vitro antiviral activity with in vivo drug exposure to identify potential anti-SARS-CoV-2 drugs. Continuous development and expansion of the database are feasible with the public availability of this database.