Clustering plasma concentration-time curves: applications of unsupervised learning in pharmacogenomics.

Pharmaceutical researchers are continually searching for techniques to improve both drug development processes and patient outcomes. An area of recent interest is the potential for machine learning (ML) applications within pharmacology. One such application not yet given close study is the unsupervised clustering of plasma concentration-time curves, hereafter, pharmacokinetic (PK) curves. In this paper, we present our findings on how to cluster PK curves by their similarity. Specifically, we find clustering to be effective at identifying similar-shaped PK curves and informative for understanding patterns within each cluster of PK curves. Because PK curves are time series data objects, our approach utilizes the extensive body of research related to the clustering of time series data as a starting point. As such, we examine many dissimilarity measures between time series data objects to find those most suitable for PK curves. We identify Euclidean distance as generally most appropriate for clustering PK curves, and we further show that dynamic time warping, Fréchet, and structure-based measures of dissimilarity like correlation may produce unexpected results. As an illustration, we apply these methods in a case study with 250 PK curves used in a previous pharmacogenomic study. Our case study finds that an unsupervised ML clustering with Euclidean distance, without any subject genetic information, is able to independently validate the same conclusions as the reference pharmacogenomic results. To our knowledge, this is the first such demonstration. Further, the case study demonstrates how the clustering of PK curves may generate insights that could be difficult to perceive solely with population level summary statistics of PK metrics.

Model-based bioequivalence approach for sparse pharmacokinetic bioequivalence studies: Model selection or model averaging?

Conventional pharmacokinetic (PK) bioequivalence (BE) studies aim to compare the rate and extent of drug absorption from a test (T) and reference (R) product using non-compartmental analysis (NCA) and the two one-sided test (TOST). Recently published regulatory guidance recommends alternative model-based (MB) approaches for BE assessment when NCA is challenging, as for long-acting injectables and products which require sparse PK sampling. However, our previous research on MB-TOST approaches showed that model misspecification can lead to inflated type I error. The objective of this research was to compare the performance of model selection (MS) on R product arm data and model averaging (MA) from a pool of candidate structural PK models in MBBE studies with sparse sampling. Our simulation study was inspired by a real case BE study using a two-way crossover design. PK data were simulated using three structural models under the null hypothesis and one model under the alternative hypothesis. MB-TOST was applied either using each of the five candidate models or following MS and MA with or without the simulated model in the pool. Assuming T and R have the same PK model, our simulation shows that following MS and MA, MB-TOST controls type I error rates at or below 0.05 and attains similar or even higher power than when using the simulated model. Thus, we propose to use MS prior to MB-TOST for BE studies with sparse PK sampling and to consider MA when candidate models have similar Akaike information criterion.

The Role of Model Master Files for Sharing, Acceptance, and Communication with FDA.

With the evolving role of Model Integrated Evidence (MIE) in generic drug development and regulatory applications, the need for improving Model Sharing, Acceptance, and Communication with the FDA is warranted. Model Master File (MMF) refers to a quantitative model or a modeling platform that has undergone sufficient model Verification & Validation to be recognized as sharable intellectual property that is acceptable for regulatory purposes. MMF provides a framework for regulatorily acceptable modeling practice, which can be used with confidence to support MIE by both the industry and the U.S. Food and Drug Administration (FDA). In 2022, the FDA and the Center for Research on Complex Generics (CRCG) hosted a virtual public workshop to discuss the best practices for utilizing modeling approaches to support generic product development. This report summarizes the presentations and panel discussions of the workshop symposium entitled "Model Sharing, Acceptance, and Communication with the FDA". The symposium and this report serve as a kick-off discussion for further utilities of MMF and best practices of utilizing MMF in drug development and regulatory submissions. The potential advantages of MMFs have garnered acknowledgment from model developers, industries, and the FDA throughout the workshop. To foster a unified comprehension of MMFs and establish best practices for their application, further dialogue and cooperation among stakeholders are imperative. To this end, a subsequent workshop is scheduled for May 2-3, 2024, in Rockville, Maryland, aiming to delve into the practical facets and best practices of MMFs pertinent to regulatory submissions involving modeling and simulation methodologies.

Safety outcomes when switching between biosimilars and reference biologics: A systematic review and meta-analysis.

Biosimilars are increasingly available for the treatment of many serious disorders, however some concerns persist about switching a patient to a biosimilar whose condition is stable while on the reference biologic. Randomized controlled studies and extension studies with a switch treatment period (STP) to or from a biosimilar and its reference biologic were identified from publicly available information maintained by the U.S. Food and Drug Administration (FDA). These findings were augmented with data from peer reviewed publications containing information not captured in FDA reviews. Forty-four STPs were identified from 31 unique studies for 21 different biosimilars. Data were extracted and synthesized following PRISMA guidelines. Meta-analysis was conducted to estimate the overall risk difference across studies. A total of 5,252 patients who were switched to or from a biosimilar and its reference biologic were identified. Safety data including deaths, serious adverse events, and treatment discontinuation showed an overall risk difference (95% CI) of -0.00 (-0.00, 0.00), 0.00 (-0.01, 0.01), -0.00 (-0.01, 0.00) across STPs, respectively. Immunogenicity data showed similar incidence of anti-drug antibodies and neutralizing antibodies in patients within a STP who were switched to or from a biosimilar to its reference biologic and patients who were not switched. Immune related adverse events such as anaphylaxis, hypersensitivity reactions, and injections site reactions were similar in switched and non-switched patients. This first systematic review using statistical methods to address the risk of switching patients between reference biologics and biosimilars finds no difference in the safety profiles or immunogenicity rates in patients who were switched and those who remained on a reference biologic or a biosimilar.

Adaptive designs for IVPT data with mixed scaled average bioequivalence.

In vitro permeation tests (IVPT) offer accurate and cost-effective development pathways for locally acting drugs, such as topical dermatological products. For assessment of bioequivalence, the FDA draft guidance on generic acyclovir 5% cream introduces a new experimental design, namely the single-dose, multiple-replicate per treatment group design, as IVPT pivotal study design. We examine the statistical properties of its hypothesis testing method-namely the mixed scaled average bioequivalence (MSABE). Meanwhile, some adaptive design features in clinical trials can help researchers make a decision earlier with fewer subjects or boost power, saving resources, while controlling the impact on family-wise error rate. Therefore, we incorporate MSABE in an adaptive design combining the group sequential design and sample size re-estimation. Simulation studies are conducted to study the passing rates of the proposed methods-both within and outside the average bioequivalence limits. We further consider modifications to the adaptive designs applied for IVPT BE trials, such as Bonferroni's adjustment and conditional power function. Finally, a case study with real data demonstrates the advantages of such adaptive methods.

Evaluation of model-based bioequivalence approach for single sample pharmacokinetic studies.

In a traditional pharmacokinetic (PK) bioequivalence (BE) study, a two-way crossover study is conducted, PK parameters (namely the area under the time-concentration curve [AUC] and the maximal concentration [ C max ]) are obtained by noncompartmental analysis (NCA), and the BE analysis is performed using the two one-sided test (TOST) method. For ophthalmic drugs, however, only one sample of aqueous humor, in one eye, per eye can be obtained in each patient, which precludes the traditional BE analysis. To circumvent this issue, the U.S. Food and Drug Administration (FDA) has proposed an approach coupling NCA with either parametric or nonparametric bootstrap (NCA bootstrap). The model-based TOST (MB-TOST) has previously been proposed and evaluated successfully for various settings of sparse PK BE studies. In this paper, we evaluate, via simulations, MB-TOST in the specific setting of single sample PK BE study and compare its performance to NCA bootstrap. We performed BE study simulations using a published PK model and parameter values and evaluated multiple scenarios, including study design (parallel or crossover), sampling times (5 or 10 spread across the dosing interval), and geometric mean ratio (of 0.8, 0.9, 1, and 1.25). Using the simulated structural PK model, MB-TOST performed similarly to NCA bootstrap for AUC. For C max , the latter tended to be conservative and less powerful. Our research suggests that MB-TOST may be considered as an alternative BE approach for single sample PK studies, provided that the PK model is correctly specified and the test drug has the same structural model as the reference drug.

Impact of model misspecification on model-based tests in PK studies with parallel design: real case and simulation studies.

This article evaluates the performance of pharmacokinetic (PK) equivalence testing between two formulations of a drug through the Two-One Sided Tests (TOST) by a model-based approach (MB-TOST), as an alternative to the classical non-compartmental approach (NCA-TOST), for a sparse design with a few time points per subject. We focused on the impact of model misspecification and the relevance of model selection for the reference data. We first analysed PK data from phase I studies of gantenerumab, a monoclonal antibody for the treatment of Alzheimer's disease. Using the original rich sample data, we compared MB-TOST to NCA-TOST for validation. Then, the analysis was repeated on a sparse subset of the original data with MB-TOST. This analysis inspired a simulation study with rich and sparse designs. With rich designs, we compared NCA-TOST and MB-TOST in terms of type I error and study power. With both designs, we explored the impact of misspecifying the model on the performance of MB-TOST and adding a model selection step. Using the observed data, the results of both approaches were in general concordance. MB-TOST results were robust with sparse designs when the underlying PK structural model was correctly specified. Using the simulated data with a rich design, the type I error of NCA-TOST was close to the nominal level. When using the simulated model, the type I error of MB-TOST was controlled on rich and sparse designs, but using a misspecified model led to inflated type I errors. Adding a model selection step on the reference data reduced the inflation. MB-TOST appears as a robust alternative to NCA-TOST, provided that the PK model is correctly specified and the test drug has the same PK structural model as the reference drug.

Application of Modeling and Simulation to Identify a Shortened Study Duration and Novel Bioequivalence Metric for a Long-Acting Intrauterine System.

An intrauterine system (IUS) can be implanted in the uterus and deliver drug directly at the site of pharmacological action. Mirena was the first FDA-approved levonorgestrel (LNG) releasing IUS without an approved generic form. Its 5-year application duration presents challenges for bioequivalence (BE) assessment using the conventional in vivo studies with pharmacokinetic and/or comparative clinical endpoints. Conventionally, along with other conditions, BE could be established if the 90% confidence interval (CI) of the ratio of geometric means of residual LNG at the end of 5 years is within the BE limits of 80.00% and 125.00%. Modeling and simulation were conducted to identify a shortened BE study duration and its corresponding BE acceptance limit that can be used as a surrogate for the conventional limit for a 5-year study. Simulation results suggest that having the 90% CI of the residual LNG 12 months post insertion within 95.00-105.26% would ensure that residual LNG amount at 5 years to be within 80.00-125.00%. This modeling and simulation practice leads to the current BE recommendation: if a test IUS is made of the same material in the same concentration and has the same physical dimensions as the Mirena, its BE could be established by showing (1) comparative physicochemical and mechanical properties; (2) comparative in vitro drug release behavior for 5 years; and (3) performance in a comparative short-term in vivo study and BE based on 90% confidence interval of test and reference ratio of residual LNG to be within 95.00-105.26% at month 12.

Efficient model-based bioequivalence testing.

The classical approach to analyze pharmacokinetic (PK) data in bioequivalence studies aiming to compare two different formulations is to perform noncompartmental analysis (NCA) followed by two one-sided tests (TOST). In this regard, the PK parameters area under the curve (AUC) and $C_{\max}$ are obtained for both treatment groups and their geometric mean ratios are considered. According to current guidelines by the U.S. Food and Drug Administration and the European Medicines Agency, the formulations are declared to be sufficiently similar if the $90\%$ confidence interval for these ratios falls between $0.8$ and $1.25 $. As NCA is not a reliable approach in case of sparse designs, a model-based alternative has already been proposed for the estimation of $\rm AUC$ and $C_{\max}$ using nonlinear mixed effects models. Here we propose another, more powerful test than the TOST and demonstrate its superiority through a simulation study both for NCA and model-based approaches. For products with high variability on PK parameters, this method appears to have closer type I errors to the conventionally accepted significance level of $0.05$, suggesting its potential use in situations where conventional bioequivalence analysis is not applicable.

New Model-Based Bioequivalence Statistical Approaches for Pharmacokinetic Studies with Sparse Sampling.

In traditional pharmacokinetic (PK) bioequivalence analysis, two one-sided tests (TOST) are conducted on the area under the concentration-time curve and the maximal concentration derived using a non-compartmental approach. When rich sampling is unfeasible, a model-based (MB) approach, using nonlinear mixed effect models (NLMEM) is possible. However, MB-TOST using asymptotic standard errors (SE) presents increased type I error when asymptotic conditions do not hold. In this work, we propose three alternative calculations of the SE based on (i) an adaptation to NLMEM of the correction proposed by Gallant, (ii) the a posteriori distribution of the treatment coefficient using the Hamiltonian Monte Carlo algorithm, and (iii) parametric random effects and residual errors bootstrap. We evaluate these approaches by simulations, for two-arms parallel and two-period, two-sequence cross-over design with rich (n = 10) and sparse (n = 3) sampling under the null and the alternative hypotheses, with MB-TOST. All new approaches correct for the inflation of MB-TOST type I error in PK studies with sparse designs. The approach based on the a posteriori distribution appears to be the best compromise between controlled type I errors and computing times. MB-TOST using non-asymptotic SE controls type I error rate better than when using asymptotic SE estimates for bioequivalence on PK studies with sparse sampling.

FDA's Generic Drug Program: Decreasing Time to Approval and Number of Review Cycles.

Implementation of the Generic Drug User Fee Amendments (GDUFA I) as part of the Food and Drug Administration Safety and Innovation Act of 2012 (Generic Drug User Fee Amendments. https://www.fda.gov/industry/fda-user-fee-programs/generic-drug-user-fee-amendments) successfully allowed the U.S. Food and Drug Administration (FDA) to modernize review of Abbreviated New Drug Applications (ANDAs) with goal dates. The goal of this study was to assess the success of GDUFA I in decreasing ANDA time to approval and the number of review cycles across the five fiscal years of GDUFA I. Results of this study underscore FDA's continuous progress within its generic drug program and highlight the ongoing collaborative communication process between FDA and ANDA applicants that must continue for the timely approval of high-quality, safe, and effective generic drugs for the American public.

Statistical considerations and impact of the FDA draft guidance for assessing adhesion with transdermal delivery systems and topical patches for ANDAs.

Until 2016, a ratio of means (ROM) non-inferiority (NI) test was recommended in FDA product-specific guidances (PSGs) to evaluate adhesion performance for prospective generic transdermal delivery systems (TDS). However, the ROM NI test had low power for well-adhering TDS, which were becoming increasingly prevalent. Mathematical proof and simulation revealed that the low power wasn't because the non-normality of adhesion data violated the normality assumption of parametric methods; it was because the ROM NI test was coupled with an adhesion scale where scores approached 0 as adhesion got better. In June 2016, FDA published a draft general guidance on TDS adhesion and recommended a new statistical approach, replacing the ROM NI test with a difference-of-means (DOM) NI test, using the same scale and primary endpoint (mean adhesion scores). An analysis of 40 TDS adhesion studies submitted in ANDAs after the publication of the 2016 draft guidance suggests that, consistent with simulation results, the new statistical approach markedly improves the low power, and thereby reduces the sample size required by the old approach for moderately to well-adhering TDS, while retaining comparable power for poorly adhering TDS. The new statistical approach thus enhances the potential approvability and patient access to well-adhering generic TDS.

Abbreviated New Drug Applications: Generic Drug User Fee Amendments Act Analysis of Application Quality Metrics.

Implementation of the first Generic Drug User Fee Amendments of 2012 (GDUFA I) provided funding to the US Food and Drug Administration (FDA) for modernizing review of the FDA/CDER Generic Drug Program. Under GDUFA I, FDA agreed to reduce the backlog of pending generic Abbreviated New Drug Applications (ANDAs), improve the efficiency of generic drug review, and reduce the number of review cycles with the goal of reducing overall time to approval. This study presents a preliminary analysis of initial filing and regulatory first actions on ANDAs during GDUFA I cohort year 3 (CY3) and cohort year 4 (CY4). It highlights initial successes and areas of improvement in the ANDA review process for both FDA and ANDA applicants to improve the efficiency of providing the public with high-quality, affordable generic drugs.

Ratio of means vs. difference of means as measures of superiority, noninferiority, and average bioequivalence.

Ratio of means (ROM) and difference of means (DOM) are often used in a superiority, noninferiority (NI), or average bioequivalence (ABE) test to evaluate whether the test mean is superior, NI, or equivalent to the reference (placebo or active control) mean. The literature provides recommendations regarding how to choose between ROM and DOM, mainly for superiority testing. In this article, we evaluated these two measures from other perspectives and cautioned the potential impact of different scoring systems/transformation for the same outcome (which is not rarely seen in practice) on the power of a ROM or DOM test for superiority, NI, or ABE. 1) For superiority, with the same margin, power remains the same for a location, scale, or combined shift (no other transformations) to scoring systems for both measures; however, for NI and ABE, different shifts can change the power of the test significantly. 2) Direction of scores (larger or smaller value indicating desirable effects) does not change the power for a DOM superiority, NI, or ABE test, but it does change the power tremendously for a ROM, NI, or ABE test. Caution should be taken when defining scoring systems. Data transformation is not encouraged in general, and if needed, should be statistically justified.

Factors associated with celecoxib and rofecoxib utilization.

BACKGROUND: The cyclooxygenase-2 (COX-2) selective nonsteroidal antiinflammatory drugs (NSAIDs) celecoxib and rofecoxib (before its removal) are marketed as having fewer gastrointestinal (GI)-related complications than nonselective NSAIDs. However, adverse reaction data suggest that the use of COX-2 selective NSAIDs is associated with clinically significant GI events. OBJECTIVE: To assess whether patients receiving celecoxib and rofecoxib have a greater underlying disease burden than patients prescribed nonselective NSAIDs. METHODS: The study population consisted of members of 11 health plans, aged >34 years, with a pharmacy claim for celecoxib or rofecoxib or a nonselective NSAID dispensed between February 1, 1999, and July 31, 2001, who had been continuously enrolled for >364 days before the dispensing date. Celecoxib and rofecoxib patients were randomly selected without replacement from a pool of eligible users in each of the 30 months. Nonselective NSAID users were randomly chosen without replacement within each month on a 2:1 ratio to cases; they could be chosen in more than one month. Univariate analyses comparing 9000 cases and 18 000 controls were performed, followed by a multiple logistic regression analysis conditioned on time. RESULTS: Increasing age, treatment by a rheumatologist or an orthopedic specialist, treatment with a high number of different medications in the past year, treatment with oral corticosteroids in the past year, and having had a previous GI bleed increased the likelihood of receiving celecoxib or rofecoxib, whereas treatment with a high number of nonselective NSAID prescriptions in the past year decreased it. Treatment with a high number of different medications was a predictor of increased prevalence of underlying diabetes mellitus and cardiovascular disease. CONCLUSIONS: Patients having a greater underlying disease burden were more likely to receive COX-2 selective NSAIDs than nonselective ones. Paradoxically, patients at higher risk for cardiovascular disease were channeled toward treatment with COX-2 selective NSAIDs, many of which may confer an increased risk of acute myocardial infarction and other adverse cardiovascular outcomes.

Validity of self-reporting of episodes of external genital warts.

To determine whether men are able to self-diagnose external genital warts (EGWs), we studied data from 1115 men with and without human immunodeficiency virus infection. Men were largely unable to accurately assess the presence of EGWs. Self-reporting of EGWs was not a sensitive tool; only 38% of men who had EGWs diagnosed by a trained examiner who used bright light and visual inspection also reported having them. When we controlled for other covariates in a multivariate model, men who had EGWs diagnosed by an examiner were 14 times less likely to show concordance between examiner findings and self-report than were men who did not have EGWs diagnosed by an examiner (odds ratio, 0.07; 95% confidence interval, 0.06-0.09). Self-diagnosis and self-assessment may not accurately reflect the presence of EGWs, and self-diagnosis should not be used in place of an examiner's findings for epidemiologic studies that seek to determine the cause of disease.