FDA Approval Summary: Enfortumab Vedotin for Locally Advanced or Metastatic Urothelial Carcinoma.

On December 18, 2019, the FDA granted accelerated approval to enfortumab vedotin-ejfv (PADCEV; Astellas and Seattle Genetics) for treatment of patients with locally advanced or metastatic urothelial cancer who have previously received a programmed cell death protein 1 or programmed death ligand 1 inhibitor, and a platinum-containing chemotherapy in the neoadjuvant/adjuvant, locally advanced or metastatic setting. Substantial evidence of effectiveness for this application is obtained from Cohort 1 of the single-arm, multicenter Study EV-201. Patients received enfortumab vedotin (EV) 1.25 mg/kg (up to a maximum dose of 125 mg) intravenously on days 1, 8, and 15 of 28-day cycles until disease progression or unacceptable toxicity. Confirmed objective response rate in the 125-patient efficacy population determined by blinded independent central review was 44% [95% confidence interval (CI), 35.1-53.2], with complete responses in 12%. Median response duration was 7.6 months (95% CI, 6.3-not estimable). Grade 3-4 adverse reactions occurred in 73% of patients. Hyperglycemia, peripheral neuropathy, ocular disorders, skin reactions, infusion site extravasations, and embryo-fetal toxicity are labeled as warnings and precautions for EV. The article summarizes the data and the FDA thought process supporting accelerated approval of EV. This approval may be contingent upon verification and description of clinical benefit in confirmatory trial(s).

FDA Approval Summary: Ivosidenib for Relapsed or Refractory Acute Myeloid Leukemia with an Isocitrate Dehydrogenase-1 Mutation.

The FDA approved ivosidenib (Tibsovo; Agios), a small-molecule inhibitor of isocitrate dehydrogenase (IDH)1 on July 20, 2018, for treatment of adults with relapsed or refractory acute myeloid leukemia (R/R AML) with susceptible IDH1 mutation as detected by an FDA-approved test. The efficacy of ivosidenib was established on the basis of complete remission (CR) + CR with partial hematologic recovery (CRh) rate, duration of CR + CRh, and conversion from transfusion dependence (TD) to transfusion independence (TI) in Study AG120-C-001, a single-arm trial. With median follow-up of 8.3 months for 174 adults with IDH1-mutated R/R AML treated with 500 mg ivosidenib daily, the CR + CRh rate was 33% [95% confidence interval (CI), 26-40], median duration of response was 8.2 (95% CI, 5.6-12) months, and conversion from TD to TI occurred in 37% of patients. These endpoints reflect short-term benefit in patients with an unmet medical need; long-term efficacy outcomes were not assessed. Serious adverse reactions (AR) in ≥5% of patients were differentiation syndrome (10%), leukocytosis (10%), and QT interval prolongation (7%). Common (≥20%) ARs of any grade were fatigue, leukocytosis, arthralgia, diarrhea, dyspnea, edema, nausea, mucositis, QT interval prolongation, rash, pyrexia, cough, and constipation. Assessment of long-term safety of ivosidenib is a condition of this approval.

Advancing Predictions of Tissue and Intracellular Drug Concentrations Using In Vitro, Imaging and Physiologically Based Pharmacokinetic Modeling Approaches.

This white paper examines recent progress, applications, and challenges in predicting unbound and total tissue and intra/subcellular drug concentrations using in vitro and preclinical models, imaging techniques, and physiologically based pharmacokinetic (PBPK) modeling. Published examples, regulatory submissions, and case studies illustrate the application of different types of data in drug development to support modeling and decision making for compounds with transporter-mediated disposition, and likely disconnects between tissue and systemic drug exposure. The goals of this article are to illustrate current best practices and outline practical strategies for selecting appropriate in vitro and in vivo experimental methods to estimate or predict tissue and plasma concentrations, and to use these data in the application of PBPK modeling for human pharmacokinetic (PK), efficacy, and safety assessment in drug development.

Predictive Performance of Physiologically-Based Pharmacokinetic Models in Predicting Drug-Drug Interactions Involving Enzyme Modulation.

BACKGROUND: Physiologically-based pharmacokinetic (PBPK) modeling in predicting metabolic drug-drug interactions (mDDIs) is routinely used in drug development. Currently, the US FDA endorses the use of PBPK to potentially support dosing recommendations for investigational drugs as enzyme substrates of mDDIs, and to inform a lack of mDDIs for investigational drugs as enzyme modulators. METHODS: We systematically evaluated the performance of PBPK modeling in predicting mDDIs published in the literature. Models developed to assess both investigational drugs as enzyme substrates (Groups 1 and 2, as being inhibited and induced, respectively) or enzyme modulators (Groups 3 and 4, as inhibitors and inducers, respectively) were evaluated. Predicted ratios of the area under the curve (AUCRs) and/or maximum plasma concentration (CmaxRs) with and without comedication were compared with the observed ratios. RESULTS: For Groups 1, 2, 3, and 4, 62, 50, 44, and 43% of model-predicted AUCRs, respectively, were within a predefined threshold of 1.25-fold of observed values (0.8-1.25x). When the threshold was widened to twofold, the values increased to 100, 80, 81, and 86% (0.5-2.0x). For Groups 3 and 4, prediction for mDDI liability (the existence or lack of mDDIs) using PBPK appears to be satisfactory. CONCLUSION: Our analysis supports the FDA's current recommendations on the use of PBPK to predict mDDIs.

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.

Predicting the Effect of CYP3A Inducers on the Pharmacokinetics of Substrate Drugs Using Physiologically Based Pharmacokinetic (PBPK) Modeling: An Analysis of PBPK Submissions to the US FDA.

BACKGROUND AND OBJECTIVE: We recently published analyses regarding the predictive performance of physiologically based pharmacokinetic (PBPK) models, submitted to the US Food and Drug Administration (FDA), for the effect of cytochrome P450 (CYP) inhibitors on the pharmacokinetics of substrate drugs. We now analyze and summarize the predictive performance of PBPK models for the effect of CYP3A inducers on a substrate's pharmacokinetics. METHODS: This analysis was based on 11 substrate PBPK models, developed by six sponsors, using a commercial PBPK software, with 13 clinical interaction studies. Four CYP3A inducers were used: rifampicin, rifabutin, carbamazepine, and efavirenz. Sponsors either directly used the software-provided inducer models or verified these models' induction magnitude prior to use. The metric for assessing predictive performance was the R predicted/observed value [R predicted/observed = (predicted mean exposure ratio)/(observed mean exposure ratio)], with the exposure ratio defined as maximum plasma concentration (C max) or area under the plasma concentration-time curve (AUC) with and without an inducer. RESULTS: In 77% (10/13; AUCR) and 83% (10/12; C max R) of the cases, the R predicted/observed values were within 1.25-fold of the observed data. Cases with R predicted/observed values >1.25-fold (>twofold for all three AUCR) were under-predictions as a result of using the PBPK software's default rifampicin model. Improved predictions were observed when the rifampicin model was modified by increasing the induction potency. CONCLUSION: Based on submissions to the FDA, and similar to our previous findings for CYP inhibition, we observed good agreement between PBPK-predicted and observed effect of CYP3A inducers on substrate pharmacokinetics. Verification of the inducer model appears to be crucial for improved predictive performance.

Predicting the effect of cytochrome P450 inhibitors on substrate drugs: analysis of physiologically based pharmacokinetic modeling submissions to the US Food and Drug Administration.

BACKGROUND AND OBJECTIVE: The US Food and Drug Administration (FDA) has seen a recent increase in the application of physiologically based pharmacokinetic (PBPK) modeling towards assessing the potential of drug-drug interactions (DDI) in clinically relevant scenarios. To continue our assessment of such approaches, we evaluated the predictive performance of PBPK modeling in predicting cytochrome P450 (CYP)-mediated DDI. METHODS: This evaluation was based on 15 substrate PBPK models submitted by nine sponsors between 2009 and 2013. For these 15 models, a total of 26 DDI studies (cases) with various CYP inhibitors were available. Sponsors developed the PBPK models, reportedly without considering clinical DDI data. Inhibitor models were either developed by sponsors or provided by PBPK software developers and applied with minimal or no modification. The metric for assessing predictive performance of the sponsors' PBPK approach was the R predicted/observed value (R predicted/observed = [predicted mean exposure ratio]/[observed mean exposure ratio], with the exposure ratio defined as [C max (maximum plasma concentration) or AUC (area under the plasma concentration-time curve) in the presence of CYP inhibition]/[C max or AUC in the absence of CYP inhibition]). RESULTS: In 81 % (21/26) and 77 % (20/26) of cases, respectively, the R predicted/observed values for AUC and C max ratios were within a pre-defined threshold of 1.25-fold of the observed data. For all cases, the R predicted/observed values for AUC and C max were within a 2-fold range. CONCLUSION: These results suggest that, based on the submissions to the FDA to date, there is a high degree of concordance between PBPK-predicted and observed effects of CYP inhibition, especially CYP3A-based, on the exposure of drug substrates.

Towards quantitation of the effects of renal impairment and probenecid inhibition on kidney uptake and efflux transporters, using physiologically based pharmacokinetic modelling and simulations.

BACKGROUND AND OBJECTIVES: The kidney is a major drug-eliminating organ. Renal impairment or concomitant use of transporter inhibitors may decrease active secretion and increase exposure to a drug that is a substrate of kidney secretory transporters. However, prediction of the effects of patient factors on kidney transporters remains challenging because of the multiplicity of transporters and the lack of understanding of their abundance and specificity. The objective of this study was to use physiologically based pharmacokinetic (PBPK) modelling to evaluate the effects of patient factors on kidney transporters. METHODS: Models for three renally cleared drugs (oseltamivir carboxylate, cidofovir and cefuroxime) were developed using a general PBPK platform, with the contributions of net basolateral uptake transport (T up,b) and apical efflux transport (T eff,a) being specifically defined. RESULTS AND CONCLUSION: We demonstrated the practical use of PBPK models to: (1) define transporter-mediated renal secretion, using plasma and urine data; (2) inform a change in the system-dependent parameter (≥10-fold reduction in the functional 'proximal tubule cells per gram kidney') in severe renal impairment that is responsible for the decreased secretory transport activities of test drugs; (3) derive an in vivo, plasma unbound inhibition constant of T up,b by probenecid (≤1 µM), based on observed drug interaction data; and (4) suggest a plausible mechanism of probenecid preferentially inhibiting T up,b in order to alleviate cidofovir-induced nephrotoxicity.