US Food and Drug Administration Approval Summary: Elacestrant for Estrogen Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Negative, ESR1-Mutated Advanced or Metastatic Breast Cancer.

PURPOSE: The US Food and Drug Administration (FDA) approved elacestrant for the treatment of postmenopausal women or adult men with estrogen receptor-positive (ER+), human epidermal growth factor receptor 2-negative (HER2-), estrogen receptor 1 (ESR1)-mutated advanced or metastatic breast cancer with disease progression after at least one line of endocrine therapy (ET). PATIENTS AND METHODS: Approval was based on EMERALD (Study RAD1901-308), a randomized, open-label, active-controlled, multicenter trial in 478 patients with ER+, HER2- advanced or metastatic breast cancer, including 228 patients with ESR1 mutations. Patients were randomly assigned (1:1) to receive either elacestrant 345 mg orally once daily (n = 239) or investigator's choice of ET (n = 239). RESULTS: In the ESR1-mut subgroup, EMERALD demonstrated a statistically significant improvement in progression-free survival (PFS) by blinded independent central review assessment (n = 228; hazard ratio [HR], 0.55 [95% CI, 0.39 to 0.77]; P value = .0005). Although the overall survival (OS) end point was not met, there was no trend toward a potential OS detriment (HR, 0.90 [95% CI, 0.63 to 1.30]) in the ESR1-mut subgroup. PFS also reached statistical significance in the intention-to-treat population (ITT, N = 478; HR, 0.70 [95% CI, 0.55 to 0.88]; P value = .0018). However, improvement in PFS in the ITT population was primarily attributed to results from patients in the ESR1-mut subgroup. More patients who received elacestrant experienced nausea, vomiting, and dyslipidemia. CONCLUSION: The approval of elacestrant in ER+, HER2- advanced or metastatic breast cancer was restricted to patients with ESR1 mutations. Benefit-risk assessment in the ESR1-mut subgroup was favorable on the basis of a statistically significant improvement in PFS in the context of an acceptable safety profile including no evidence of a potential detriment in OS. By contrast, the benefit-risk assessment in patients without ESR1 mutations was not favorable. Elacestrant is the first oral estrogen receptor antagonist to receive FDA approval for patients with ESR1 mutations.

FDA Approval Summary: Pirtobrutinib for Relapsed or Refractory Mantle Cell Lymphoma.

In January 2023, the FDA granted accelerated approval to pirtobrutinib for the treatment of adult patients with relapsed or refractory mantle cell lymphoma (MCL) after at least two lines of systemic therapy, including a Bruton tyrosine kinase (BTK) inhibitor. Approval was based on BRUIN, a single-arm study of pirtobrutinib monotherapy in patients with B-cell malignancies. Efficacy was based on independent review committee-assessed overall response rate (ORR) supported by durability of response in 120 patients with relapsed or refractory MCL who had received a prior BTK inhibitor and received the approved pirtobrutinib dosage of 200 mg once daily. The ORR was 50% [95% confidence interval (CI), 41-59], and the complete response rate was 13% (95% CI, 7-20), with an estimated median duration of response of 8.3 months. The most common nonhematologic adverse reactions were fatigue, musculoskeletal pain, diarrhea, edema, dyspnea, pneumonia, and bruising. Warnings and Precautions in labeling include infection, hemorrhage, cytopenias, atrial arrhythmias, and second primary malignancies. Postmarketing studies were required to evaluate longer-term safety of pirtobrutinib and to verify the clinical benefit of pirtobrutinib. This article summarizes key aspects of the regulatory review, including the indication statement, efficacy and safety considerations, and postmarketing requirements.

Utility of Physiologically Based Pharmacokinetic Modeling to Investigate the Impact of Physiological Changes of Pregnancy and Cancer on Oncology Drug Pharmacokinetics.

BACKGROUND: The treatment of cancer during pregnancy remains challenging with knowledge gaps in drug dosage, safety, and efficacy due to the under-representation of this population in clinical trials. Our aim was to investigate physiological changes reported in both pregnancy and cancer populations into a PBPK modeling framework that allows for a more accurate estimation of PK changes in pregnant patients with cancer. METHODS: Paclitaxel and docetaxel were selected to validate a population model using clinical data from pregnant patients with cancer. The validated population model was subsequently used to predict the PK of acalabrutinib in pregnant patients with cancer. RESULTS: The Simcyp pregnancy population model reasonably predicted the PK of docetaxel in pregnant patients with cancer, while a modified model that included a 2.5-fold increase in CYP2C8 abundance, consistent with the increased expression during pregnancy, was needed to reasonably predict the PK of paclitaxel in pregnant patients with cancer. Changes in protein binding levels of patients with cancer had a minimal impact on the predicted clearance of paclitaxel and docetaxel. PBPK modeling predicted approximately 60% lower AUC and Cmax for acalabrutinib in pregnant versus non-pregnant patients with cancer. CONCLUSIONS: Our results suggest that PBPK modeling is a promising approach to investigate the effects of pregnancy and cancer on the PK of oncology drugs and potentially inform dosing for pregnant patients with cancer. Further evaluation and refinement of the population model are needed for pregnant patients with cancer with additional compounds and clinical PK data.

Physiologically Based Pharmacokinetics Modeling in the Neonatal Population-Current Advances, Challenges, and Opportunities.

Physiologically based pharmacokinetic (PBPK) modeling is an approach to predicting drug pharmacokinetics, using knowledge of the human physiology involved and drug physiochemical properties. This approach is useful when predicting drug pharmacokinetics in under-studied populations, such as pediatrics. PBPK modeling is a particularly important tool for dose optimization for the neonatal population, given that clinical trials rarely include this patient population. However, important knowledge gaps exist for neonates, resulting in uncertainty with the model predictions. This review aims to outline the sources of variability that should be considered with developing a neonatal PBPK model, the data that are currently available for the neonatal ontogeny, and lastly to highlight the data gaps where further research would be needed.

Landscape and Regulatory Perspective on Oncology Drugs in Pregnancy.

Cancers affecting pregnant women include breast cancer, melanoma, thyroid cancer, cervical cancer, lymphomas, and leukemias. The medical management of cancer during pregnancy with molecularly targeted oncology drugs remains quite challenging, with knowledge gaps about the drugs' safety and efficacy due to exclusion of pregnant women from cancer clinical trials, discontinuation of individuals who become pregnant during clinical trials, and limited information on appropriate dosing of molecularly targeted oncology drugs during pregnancy. Physiological changes occur during pregnancy and may result in alterations in the absorption, distribution, metabolism, and excretion of drugs used in pregnant women. Physiologically based pharmacokinetic modeling that incorporates physiological changes induced by both the cancer disease state and pregnancy has the potential to inform dosing of molecularly targeted oncology drugs for pregnant women, improve our understanding of the pharmacokinetic changes associated with pregnancy in patients with cancer, facilitate the design of potential studies of molecularly targeted oncology drugs in pregnant women to support dosing recommendations, and provide model-informed pharmacokinetic data to support regulatory decision making.

FDA Approval Summary: Capmatinib and Tepotinib for the Treatment of Metastatic NSCLC Harboring MET Exon 14 Skipping Mutations or Alterations.

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.

Model-Informed Drug Development Approaches to Assist New Drug Development in the COVID-19 Pandemic.

Leveraging limited clinical and nonclinical data through modeling approaches facilitates new drug development and regulatory decision making amid the coronavirus disease 2019 (COVID-19) pandemic. Model-informed drug development (MIDD) is an essential tool to integrate those data and generate evidence to (i) provide support for effectiveness in repurposed or new compounds to combat COVID-19 and dose selection when clinical data are lacking; (ii) assess efficacy under practical situations such as dose reduction to overcome supply issues or emergence of resistant variant strains; (iii) demonstrate applicability of MIDD for full extrapolation to adolescents and sometimes to young pediatric patients; and (iv) evaluate the appropriateness for prolonging a dosing interval to reduce the frequency of hospital visits during the pandemic. Ongoing research activities of MIDD reflect our continuous effort and commitment in bridging knowledge gaps that leads to the availability of effective treatments through innovation. Case examples are presented to illustrate how MIDD has been used in various stages of drug development and has the potential to inform regulatory decision making.

Whole Body PBPK Modeling of Remdesivir and Its Metabolites to Aid in Estimating Active Metabolite Exposure in the Lung and Liver in Patients With Organ Dysfunction.

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.

Characterization of Plasma Protein Alterations in Pregnant and Postpartum Individuals Living With HIV to Support Physiologically-Based Pharmacokinetic Model Development.

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.

Anti-SARS-CoV-2 Repurposing Drug Database: Clinical Pharmacology Considerations.

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 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 are feasible with the public availability of this database.

Usage of In Vitro Metabolism Data for Drug-Drug Interaction in Physiologically Based Pharmacokinetic Analysis Submissions to the US Food and Drug Administration.

The key parameters necessary to predict drug-drug interactions (DDIs) are intrinsic clearance (CLint ) and fractional contribution of the metabolizing enzyme toward total metabolism (fm ). Herein, we summarize the accumulated knowledge from 53 approved new drug applications submitted to the Office of Clinical Pharmacology, US Food and Drug Administration, from 2016 to 2018 that contained physiologically based pharmacokinetic (PBPK) models to understand how in vitro data are used in PBPK models to assess drug metabolism and predict DDIs. For evaluation of CLint and fm , 29 and 20 new drug applications were included for evaluation, respectively. For CLint , 86.2% of the PBPK models used modified values based on in vivo data with modifications ranging from -82.5% to 2752.5%. For fm , 45.0% of the models used modified values with modifications ranging from -28% to 178.6%. When values for CLint were used from in vitro testing without modification, the model resulted in up to a 14.3-fold overprediction of the area under the concentration-time curve of the substrate. When values for fm from in vitro testing were used directly, the model resulted in up to a 2.9-fold underprediction of its DDI magnitude with an inducer, and up to a 1.7-fold overprediction of its DDI magnitude with an inhibitor. Our analyses suggested that the in vitro system usually provides a reasonable estimation of fm when the drug metabolism by a given CYP pathway is more than 70% of the total clearance. In vitro experiments provide important information about basic PK properties of new drugs and can serve as a starting point for building a PBPK model. However, key PBPK parameters such as CLint and fm still need to be optimized based on in vivo data.

Application of PBPK Modeling and Simulation for Regulatory Decision Making and Its Impact on US Prescribing Information: An Update on the 2018-2019 Submissions to the US FDA's Office of Clinical Pharmacology.

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.

Physiologically Based Pharmacokinetic Modeling in Regulatory Science: An Update From the U.S. Food and Drug Administration's Office of Clinical Pharmacology.

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.

A regulatory science viewpoint on botanical-drug interactions.

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.

The Application of Physiologically Based Pharmacokinetic Modeling to Predict the Role of Drug Transporters: Scientific and Regulatory Perspectives.

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.