Clinical Pharmacology of the Antibody-Drug Conjugate Enfortumab Vedotin in Advanced Urothelial Carcinoma and Other Malignant Solid Tumors.

Enfortumab vedotin is an antibody-drug conjugate comprised of a human monoclonal antibody directed to Nectin-4 and monomethyl auristatin E (MMAE), a microtubule-disrupting agent. The objectives of this review are to summarize the clinical pharmacology of enfortumab vedotin monotherapy and demonstrate that the appropriate dose has been selected for clinical use. Pharmacokinetics (PK) of enfortumab vedotin (antibody-drug conjugate and total antibody) and free MMAE were evaluated in five clinical trials of patients with locally advanced or metastatic urothelial carcinoma (n = 748). Intravenous enfortumab vedotin 0.5-1.25 mg/kg on days 1, 8, and 15 of a 28-day cycle showed linear, dose-proportional PK. No significant differences in exposure or safety of enfortumab vedotin and free MMAE were observed in mild, moderate, or severe renal impairment versus normal renal function. Patients with mildly impaired versus normal hepatic function had a 37% increase in area under the concentration-time curve (0-28 days), a 31% increase in maximum concentration of free MMAE, and a similar adverse event profile. No clinically significant PK differences were observed based on race/ethnicity with weight-based dosing, and no clinically meaningful QT prolongation was observed. Concomitant use with dual P-glycoprotein and strong cytochrome P450 3A4 inhibitors may increase MMAE exposure and the risk of adverse events. Approximately 3% of patients developed antitherapeutic antibodies against enfortumab vedotin 1.25 mg/kg. These findings support enfortumab vedotin 1.25 mg/kg monotherapy on days 1, 8, and 15 of a 28-day cycle. No dose adjustments are required for patients with renal impairment or mild hepatic impairment, or by race/ethnicity.

Training the next generation of pharmacometric modelers: a multisector perspective.

The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970's and early 1980's and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations.

Prospective approaches to gene therapy computational modeling - spotlight on viral gene therapy.

Clinical studies have found there still exists a lack of gene therapy dose-toxicity and dose-efficacy data that causes gene therapy dose selection to remain elusive. Model informed drug development (MIDD) has become a standard tool implemented throughout the discovery, development, and approval of pharmaceutical therapies, and has the potential to inform dose-toxicity and dose-efficacy relationships to support gene therapy dose selection. Despite this potential, MIDD approaches for gene therapy remain immature and require standardization to be useful for gene therapy clinical programs. With the goal to advance MIDD approaches for gene therapy, in this review we first provide an overview of gene therapy types and how they differ from a bioanalytical, formulation, route of administration, and regulatory standpoint. With this biological and regulatory background, we propose how MIDD can be advanced for AAV-based gene therapies by utilizing physiological based pharmacokinetic modeling and quantitative systems pharmacology to holistically inform AAV and target protein dynamics following dosing. We discuss how this proposed model, allowing for in-depth exploration of AAV pharmacology, could be the key the field needs to treat these unmet disease populations.

Physiologically based pharmacokinetic model to predict drug-drug interactions with the antibody-drug conjugate enfortumab vedotin.

Enfortumab vedotin is an antibody-drug conjugate (ADC) comprised of a Nectin-4-directed antibody and monomethyl auristatin E (MMAE), which is primarily eliminated through P-glycoprotein (P-gp)-mediated excretion and cytochrome P450 3A4 (CYP3A4)-mediated metabolism. A physiologically based pharmacokinetic (PBPK) model was developed to predict effects of combined P-gp with CYP3A4 inhibitor/inducer (ketoconazole/rifampin) on MMAE exposure when coadministered with enfortumab vedotin and study enfortumab vedotin with CYP3A4 (midazolam) and P-gp (digoxin) substrate exposure. A PBPK model was built for enfortumab vedotin and unconjugated MMAE using the PBPK simulator ADC module. A similar model was developed with brentuximab vedotin, an ADC with the same valine-citrulline-MMAE linker as enfortumab vedotin, for MMAE drug-drug interaction (DDI) verification using clinical data. The DDI simulation predicted a less-than-2-fold increase in MMAE exposure with enfortumab vedotin plus ketoconazole (MMAE geometric mean ratio [GMR] for maximum concentration [Cmax], 1.15; GMR for area under the time-concentration curve from time 0 to last quantifiable concentration [AUClast], 1.38). Decreased MMAE exposure above 50% but below 80% was observed with enfortumab vedotin plus rifampin (MMAE GMR Cmax, 0.72; GMR AUClast, 0.47). No effect of enfortumab vedotin on midazolam or digoxin systemic exposure was predicted. Results suggest that combination enfortumab vedotin, P-gp, and a CYP3A4 inhibitor may result in increased MMAE exposure and patients should be monitored for potential adverse effects. Combination P-gp and a CYP3A4 inducer may result in decreased MMAE exposure. No exposure change is expected for CYP3A4 or P-gp substrates when combined with enfortumab vedotin.ClinicalTrials.gov identifier Not applicable.

Physiologically-based pharmacokinetic modeling to predict drug-drug interaction of enzalutamide with combined P-gp and CYP3A substrates.

Enzalutamide is known to strongly induce cytochrome P450 3A4 (CYP3A4). Furthermore, enzalutamide showed induction and inhibition of P-glycoprotein (P-gp) in in vitro studies. A clinical drug-drug interaction (DDI) study between enzalutamide and digoxin, a typical P-gp substrate, suggested enzalutamide has weak inhibitory effect on P-gp substrates. Direct oral anticoagulants (DOACs), such as apixaban and rivaroxaban, are dual substrates of CYP3A4 and P-gp, and hence it is recommended to avoid co-administration of these DOACs with combined P-gp and strong CYP3A inducers. Enzalutamide's net effect on P-gp and CYP3A for apixaban and rivaroxaban plasma exposures is of interest to physicians who treat patients for venous thromboembolism with prostate cancer. Accordingly, a physiologically-based pharmacokinetic (PBPK) analysis was performed to predict the magnitude of DDI on apixaban and rivaroxaban exposures in the presence of 160 mg once-daily dosing of enzalutamide. The PBPK models of enzalutamide and M2, a major metabolite of enzalutamide which also has potential to induce CYP3A and P-gp and inhibit P-gp, were developed and verified as perpetrators of CYP3A-and P-gp-mediated interaction. Simulation results predicted a 31% decrease in AUC and no change in Cmax for apixaban and a 45% decrease in AUC and a 25% decrease in Cmax for rivaroxaban when 160 mg multiple doses of enzalutamide were co-administered. In summary, enzalutamide is considered to decrease apixaban and rivaroxaban exposure through the combined effects of CYP3A induction and net P-gp inhibition. Concurrent use of these drugs warrants careful monitoring for efficacy and safety.

A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development.

Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E-R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E-R analysis in oncology clinical drug development are and what metrics of exposure should be considered.

Pharmacokinetic comparability between two populations using nonlinear mixed effect models: a Monte Carlo study.

'Are two populations the same or are they different' is a question that is often faced in clinical pharmacology trials e.g., a pharmacokinetic trial studying a particular drug in racially different groups. To address this question, concentration-time data were simulated from a reference and test population, where in the latter the clearance, sample size, and sampling design were systematically varied. It was of interest to determine whether the estimates of clearance from the two groups were the same or different. Two approaches were used to estimate the empirical Bayes estimates (EBEs) for clearance. One approach developed a population pharmacokinetic model for the reference population and the EBEs for the reference population were estimated from this model. The parameters of the reference population were fixed to their maximum likelihood estimates. The model was then applied to the test population dataset to estimate the EBEs of the test population using the MAXEVAL = 0 option in NONMEM. A second approach, the theta approach, combined the reference and test datasets into a single dataset and used population as a covariate in the model; the EBEs were estimated from this combined model. The power and type I error rate of each approach were calculated for each treatment combination using a variety of statistical tests to determine whether there was a difference in the distribution of the EBEs in the reference population compared to the test population. Our results suggest that either MAXEVAL or theta approaches can be used with informative sampling designs. In addition to reasonable power and type I error, both approaches gave almost identical results under a dense sampling design. To statistically compare the distribution of EBEs of pharmacokinetic parameters from a reference group to that of a test group, a T-test and DTS eCDF test are equally useful.

Correction: Phase II Study Evaluating 2 Dosing Schedules of Oral Foretinib (GSK1363089), cMET/VEGFR2 Inhibitor, in Patients with Metastatic Gastric Cancer.

[This corrects the article DOI: 10.1371/journal.pone.0054014.].

Two heads are better than one: current landscape of integrating QSP and machine learning : An ISoP QSP SIG white paper by the working group on the integration of quantitative systems pharmacology and machine learning.

Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer 'omics' data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP + ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices.

Correction: Phase II Study Evaluating 2 Dosing Schedules of Oral Foretinib (GSK1363089), cMET/VEGFR2 Inhibitor, in Patients with Metastatic Gastric Cancer.

[This corrects the article DOI: 10.1371/journal.pone.0054014.].

The application of extreme value theory to pharmacometrics.

Clinical trials are often analyzed by examining the means, e.g., what is the mean treatment effect or what is the mean treatment difference, but there are times when analysis of the maximums (or minimums) are of interest. For instance, what is the highest heart rate that could be observed or what the smallest treatment effect that could be expected? While inference on the means is based on the central limit theorem, the corresponding theorem for maximums or minimums is the Fisher-Tippett theorem, also called the extreme value theorem (EVT). This manuscript will introduce EVT to pharmacometricians, particularly block maxima analysis and peak over threshold analysis, and provide examples for how it can be applied to pharmacometric data, particularly the analysis of pharmacokinetics and ECG safety data, like QTcF intervals.