Pharmacokinetic Models for Inhaled Fluticasone Propionate and Salmeterol Xinafoate to Quantify Batch-to-Batch Variability.

Advair Diskus is an essential treatment for asthma and chronic obstructive pulmonary disease. It is a dry powder inhaler with a combination of fluticasone propionate (FP) and salmeterol xinafoate (SX). However, the pharmacokinetics (PK) batch-to-batch variability of the reference-listed drug (RLD) hindered its generic product development. This work developed the PK models for inhaled FP and SX that could represent potential batch variability. Two batches each of the reference and the test product (R1, R2, T1, T2) of Advair Diskus (100 µg FP/50 µg SX inhalation) were administered to 60 healthy subjects in a 4-period, 4-sequence crossover study. The failure of the bioequivalence (BE) between R1 and R2 confirmed the high between-batch variability of the RLD. Non-linear mixed effect modeling was used to estimate the population mean PK parameters for each batch. For FP, a 2-compartment model with a sequential dual zero-order absorption best described the PK profile. For SX, a 2-compartment model with a first-order absorption model best fit the data. Both models were able to capture the plasma concentration, the maximum concentration, and the total exposure (AUCinf) adequately for each batch, which could be used to simulate the BE study in the future. In vitro properties were also measured for each batch, and the batch with a higher fraction of the fine particle (diameter < 1 µm, < 2 µm) had a higher AUCinf. This positive correlation for both FP and SX could potentially assist the batch selection for the PK BE study.

A unified dataset for the city-scale traffic assignment model in 20 U.S. cities.

City-scale traffic data, such as traffic flow, speed, and density on every road segment, are the foundation of modern urban research. However, accessing such data on a city scale is challenging due to the limited number of sensors and privacy concerns. Consequently, most of the existing traffic datasets are typically limited to small, specific urban areas with incomplete data types, hindering the research in urban studies, such as transportation, environment, and energy fields. It still lacks a city-scale traffic dataset with comprehensive data types and satisfactory quality that can be publicly available across cities. To address this issue, we propose a unified approach for producing city-scale traffic data using the classic traffic assignment model in transportation studies. Specifically, the inputs of our approach are sourced from open public databases, including road networks, traffic demand, and travel time. Then the approach outputs comprehensive and validated citywide traffic data on the entire road network. In this study, we apply the proposed approach to 20 cities in the United States, achieving an average correlation coefficient of 0.79 in average travel time and an average relative error of 5.16% and 10.47% in average travel speed when compared with the real-world data.

Discovery of Novel 4-Hydroxyquinazoline Derivatives: In Silico, In Vivo and In Vitro Studies Using Primary PARPi-Resistant Cell Lines.

A series of novel 4-Hydroxyquinazoline derivatives were designed and synthesized to enhance sensitivity in primary PARPi-resistant cells. Among them, the compound B1 has been found to have superior cytotoxicity in primary PARPi-resistant HCT-15 and HCC1937 cell lines, and dose-dependently suppressed the intracellular PAR formation and enhanced the γH2AX aggregation. Mechanistic study showed that B1 stimulated the formation of intracellular ROS and the depolarization of the mitochondrial membrane, which could increase apoptosis and cytotoxicity. An in vivo study showed that B1 significantly suppressed tumor growth at a dose of 25 mg/kg, and an acute toxicity study confirmed its safety. Molecular docking and dynamics simulations revealed that hydrogen bonding between B1 and ASP766 may be helpful to enhance anti-drug resistance ability. This study suggests that B1 is a potent PARP inhibitor that can overcome PARPi resistance and deserves further investigation.

pyDarwin: A Machine Learning Enhanced Automated Nonlinear Mixed-Effect Model Selection Toolbox.

pyDarwin is an open-source Python package for nonlinear mixed-effect model selection. pyDarwin combines machine-learning algorithms and NONMEM to perform a global search for the optimal model in a user-defined model search space. Compared with traditional stepwise search, pyDarwin provides an efficient platform for conducting an objective, robust, less labor-intensive model selection process without compromising model interpretability. In this tutorial, we will begin by introducing the essential components and concepts within the package. Subsequently, we will provide an overview of the pyDarwin modeling workflow and the necessary files needed for model selection. To illustrate the entire process, we will conclude with an example utilizing quetiapine clinical data.

Design, Synthesis, and Biological Evaluation of Novel Thioureidobenzamide (TBA) Derivatives as HBV Capsid Assembly Modulators.

Hepatitis B virus (HBV) capsid assembly modulators (CAMs) represent a promising therapeutic approach for the treatment of HBV infection. In this study, we designed and synthesized five series of benzamide derivatives based on a multisite-binding strategy at the tolerant region and diversity modification in the solvent-exposed region. Among them, thioureidobenzamide compound 17i exhibited significantly increased anti-HBV activity in HepAD38 (EC50 = 0.012 µM) and HBV-infected HLCZ01 cells (EC50 = 0.033 µM). Moreover, 17i displayed a better inhibitory effect on the assembly of HBV capsid protein compared with NVR 3-778 and a inhibitory effect similar to the clinical drug GLS4. In addition, 17i showed moderate metabolic stability in human microsomes, had excellent oral bioavailability in Sprague-Dawley (SD) rats, and inhibited HBV replication in the HBV carrier mice model, which could be considered as a promising candidate drug for further development.

Effect of Omeprazole Administration on the Pharmacokinetics of Oral Extended-Release Nifedipine in Healthy Subjects.

Oral extended-release (ER) dosage forms have been used to sustain blood drug levels, reduce adverse events, and improve patient compliance. We investigated potential effects of comedication on pharmacokinetic exposure of nifedipine ER products with different formulation designs and manufacturing processes. A clinical study compared a generic version of nifedipine ER tablet with pH-dependent dissolution behavior with an osmotic pump product with pH independent drug release under fasting condition. In this study, two nifedipine tablet products were tested with or without short-term omeprazole comedication in healthy subjects. Seven-day administration of omeprazole before nifedipine dosing significantly increased the gastric pH, and subsequently increased the geometric least square (LS) means of area under the concentration-time curve from time zero to the last measurable timepoint (AUC0-t ) and maximum plasma concentration (Cmax ) of nifedipine to 132.6% (90% confidence interval (CI): 120.6-145.7%) and 112.8% (90% CI: 100.8-126.3%) for pH-dependent ER tablets, and 120.6% (90% CI: 109.7-132.5%) and 122.5% (90% CI: 113.7-131.9%) for the pH-independent ER tablets, respectively. Similar extent of increase in AUC0-t and Cmax was confirmed in the subpopulations whose gastric pH was ≥ 4 or ≤ 3 in subjects with or without omeprazole administration. Given that similar increases in drug exposures were observed for both pH-dependent and pH-independent nifedipine formulations and the geometric LS mean ratios were between 112% and 133% with and without short-term omeprazole comedication, the gastric pH may have limited effects on omeprazole-induced nifedipine PK changes on the tested formulations. The inhibition of cytochrome P450 3A4 activity may play a significant role causing nifedipine exposure changes for both formulations, which would warrant additional assessment.

Integration of Biorelevant Pediatric Dissolution Methodology into PBPK Modeling to Predict In Vivo Performance and Bioequivalence of Generic Drugs in Pediatric Populations: a Carbamazepine Case Study.

This study investigated the impact of gastro-intestinal fluid volume and bile salt (BS) concentration on the dissolution of carbamazepine (CBZ) immediate release (IR) 100 mg tablets and to integrate these in vitro biorelevant dissolution profiles into physiologically based pharmacokinetic modelling (PBPK) in pediatric and adult populations to determine the biopredictive dissolution profile. Dissolution profiles of CBZ IR tablets (100 mg) were generated in 50-900 mL biorelevant adult fasted state simulated gastric and intestinal fluid (Ad-FaSSGF and Ad-FaSSIF), also in three alternative compositions of biorelevant pediatric FaSSGF and FaSSIF medias at 200 mL. This study found that CBZ dissolution was poorly sensitive to changes in the composition of the biorelevant media, where dissimilar dissolution (F2 = 46.2) was only observed when the BS concentration was changed from 3000 to 89 µM (Ad-FaSSIF vs Ped-FaSSIF 50% 14 BS). PBPK modeling demonstrated the most predictive dissolution volume and media composition to forecast the PK was 500 mL of Ad-FaSSGF/Ad-FaSSIF media for adults and 200 mL Ped-FaSSGF/FaSSIF media for pediatrics. A virtual bioequivalence simulation was conducted by using Ad-FaSSGF and/or Ad-FaSSIF 500 mL or Ped-FaSSGF and/or Ped-FaSSIF 200 mL dissolution data for CBZ 100 mg (reference and generic test) IR product. The CBZ PBPK models showed bioequivalence of the product. This study demonstrates that the integration of biorelevant dissolution data can predict the PK profile of a poorly soluble drug in both populations. Further work using more pediatric drug products is needed to verify biorelevant dissolution data to predict the in vivo performance in pediatrics.

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.

Comparing a Bayesian Approach (BEST) with the Two One-Sided t-Tests (TOSTs) for Bioequivalence Studies.

The two one-sided t-tests (TOST) procedure has been used to evaluate average bioequivalence (BE). As a regulatory standard, it is crucial that TOST distinguish BE from not-BE (NBE) when BE data are not lognormal. TOST was compared with a Bayesian procedure (BEST by Kruschke) in simulated datasets of test/reference ratios (T/R) which were BE and NBE, wherein (1) log(T/R) or T-R were normally distributed, (2) sample sizes ranged 10-50, and (3) extreme log(T/R) or T-R values were randomly included in datasets. The 90% "credible interval" (CrI) from BEST is a Bayesian alternative of the 90% confidence interval (CI) of TOST and it can be derived from a posterior distribution that is more reflective of the observed mean log(T/R) distribution that often deviates from normality. In the absence of extreme T/R values, both methods agreed BE when observed T/R were lognormal. BEST more accurately concluded BE or NBE, while requiring fewer subjects, when observed log(T/R) or T-R were normal in the presence of extreme values. Overall, TOST and BEST perform comparably on lognormal T/R, while BEST is more accurate, requiring fewer subjects when datasets are normal for T-R or contain extreme values. Of note, the normally distributed datasets only rarely contain extreme values. Our results imply that when BEST and TOST yield different BE assessment results from bioequivalent products, TOST may disadvantage applicants when T/R are not lognormal and/or include extreme T/R values. Application of BEST can address the situation when T/R are not lognormal or include extreme data values. Application of BEST to BE data can be considered a useful alternative to TOST for evaluation of BE and for efficient development of BE formulations.

Usage of Compartmental Models in Predicting COVID-19 Outbreaks.

Accurately predicting the spread of the SARS-CoV-2, the cause of the COVID-19 pandemic, is of great value for global regulatory authorities to overcome a number of challenges including medication shortage, outcome of vaccination, and control strategies planning. Modeling methods that are used to simulate and predict the spread of COVID-19 include compartmental model, structured metapopulations, agent-based networks, deep learning, and complex network, with compartmental modeling as one of the most widely used methods. Compartmental model has two noteworthy features, a flexible framework that allows users to easily customize the model structure and its high adaptivity that allows well-matured approaches (e.g., Bayesian inference and mixed-effects modeling) to improve parameter estimation. We retrospectively evaluated the prediction performances of the compartmental models on the CDC COVID-19 Mathematical Modeling webpage based on data collected between August 2020 and February 2021, and subsequently discussed in detail their corresponding model enhancement. Finally, we presented examples using the compartmental models to assist policymaking. By evaluating all models in parallel, we systemically evaluated the performance and evolution of using compartmental models for COVID-19 pandemic prediction. In summary, as a 100-year-old epidemic approach, the compartmental model presents a powerful tool that is extremely adaptive and can be readily customized and implemented to address new data or emerging needs during a pandemic.

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.

Design, synthesis and biological evaluation of novel dihydrobenzodioxine derivatives as HBV capsid protein inhibitors.

Capsid assembly modulators (CAMs) have recently been revealed to be effective in blocking HBV replication. HBV capsid protein inhibitors reduce and ultimately eliminate HBV by inhibiting virus replication and blocking hepatocyte infection. Sulfonamides are synthetic functional groups in development of different kinds of drugs. Sulfonyl benzamide clinical drugs NVR 3-778 and BA-38017 are lead compounds in discovery of antiviral compounds with increased activity and reduced cytotoxicity by drug design strategies including pharmacophore hybrid, bioisosterism and scaffold hopping. In current study, three series of target compounds were synthesized, and their anti-HBV activity was evaluated against HepAD38 cells. Compound 5a (EC50 = 0.50 ± 0.07 µM, CC50 = 48.16 ± 9.15 µM) showed better anti-HBV DNA replication activity than the lead compound BA-38017, and showed good inhibitory effect on the assembly of HBV capsid protein compared with the clinical drug NVR 3-778. In addition, preliminary structure-activity relationship (SAR) and molecular docking studies were conducted to explore potential interactions and binding modes between compounds and target proteins, which may help researchers to find more effective anti-HBV drugs.

Tropical cyclone-blackout-heatwave compound hazard resilience in a changing climate.

Tropical cyclones (TCs) have caused extensive power outages. The impacts of TC-caused blackouts may worsen in the future as TCs and heatwaves intensify. Here we couple TC and heatwave projections and power outage and recovery process analysis to investigate how TC-blackout-heatwave compound hazard risk may vary in a changing climate, with Harris County, Texas as an example. We find that, under the high-emissions scenario RCP8.5, long-duration heatwaves following strong TCs may increase sharply. The expected percentage of Harris residents experiencing at least one longer-than-5-day TC-blackout-heatwave compound hazard in a 20-year period could increase dramatically by a factor of 23 (from 0.8% to 18.2%) over the 21st century. We also reveal that a moderate enhancement of the power distribution network can significantly mitigate the compound hazard risk. Thus, climate adaptation actions, such as strategically undergrounding distribution network and developing distributed energy sources, are urgently needed to improve coastal power system resilience.

Quantitative methods and modeling to assess COVID-19-interrupted in vivo pharmacokinetic bioequivalence studies with two reference batches.

The coronavirus disease 2019 (COVID-19) has presented unprecedented challenges to the generic drug development, including interruptions in bioequivalence (BE) studies. Per guidance published by the US Food and Drug Administration (FDA) during the COVID-19 public health emergency, any protocol changes or alternative statistical analysis plan for COVID-19-interrupted BE study should be accompanied with adequate justifications and not lead to biased equivalence determination. In this study, we used a modeling and simulation approach to assess the potential impact of study outcomes when two different batches of a Reference Standard (RS) were to be used in an in vivo pharmacokinetic BE study due to the RS expiration during the COVID-19 pandemic. Simulations were performed with hypothetical drugs under two scenarios: (1) uninterrupted study using a single batch of an RS, and (2) interrupted study using two batches of an RS. The acceptability of BE outcomes was evaluated by comparing the results obtained from interrupted studies with those from uninterrupted studies. The simulation results demonstrated that using a conventional statistical approach to evaluate BE for COVID-19-interrupted studies may be acceptable based on the pooled data from two batches. An alternative statistical method which includes a "batch" effect to the mixed effects model may be used when a significant "batch" effect was found in interrupted four-way crossover studies. However, such alternative method is not applicable for interrupted two-way crossover studies. Overall, the simulated scenarios are only for demonstration purpose, the acceptability of BE outcomes for the COVID19-interrupted studies could be case-specific.

A randomized, cross-over trial of metoprolol succinate formulations to evaluate PK and PD end points for therapeutic equivalence.

There are limited comparison data throughout the dosing interval for generic versus brand metoprolol extended-release (ER) tablets. We compared the pharmacokinetics (PKs) and pharmacodynamics of brand name versus two generic formulations (drugs 1 and 2) of metoprolol ER tablets with different time to maximum concentration (Tmax ) in adults with hypertension. Participants were randomized to equal drug doses (50-150 mg/day) administered in one of two sequences (brand-drug1-brand-drug2 or brand-drug2-brand-drug1) and completed 24-h PK, digital heart rate (HR), ambulatory blood pressure (BP), and HR studies after taking each formulation for greater than or equal to 7 days. Metoprolol concentrations were determined by liquid chromatography tandem mass spectrometry, with noncompartmental analysis performed to obtain PK parameters in Phoenix WinNonlin. Heart rate variability (HRV) low-to-high frequency ratio was determined per quartile over the 24-h period. Thirty-six participants completed studies with the brand name and at least one generic product. Among 30 participants on the 50 mg dose, the primary PK end points of area under the concentration-time curve and Cmax were similar between products; Tmax was 6.1 ± 3.6 for the brand versus 3.5 ± 4.9 for drug 1 (p = 0.019) and 9.6 ± 3.2 for drug 2 (p < 0.001). Among all 36 participants, 24-h BPs and HRs were similar between products. Mean 24-h HRV low-to-high ratio was also similar for drug 1 (2.04 ± 1.35), drug 2 (1.86 ± 1.35), and brand (2.04 ± 1.77), but was more sustained over time for the brand versus drug 1 (drug × quartile interaction p = 0.017). Differences in Tmax between metoprolol ER products following repeated doses may have implications for drug effects on autonomic balance over the dosing interval.

Discovery of PHGDH inhibitors by virtual screening and preliminary structure-activity relationship study.

Phosphoglycerate dehydrogenase (PHGDH) is abnormally expressed in numerous malignant tumor cells and catalyzes the first step of serine biosynthesis, thus becoming a key drug target for antitumor treatment. In this study, compound B2 bearing a benzene-1,3-diamine scaffold was identified by structure-based virtual screening as a novel PHGDH inhibitor with moderate enzymatic activity. The structure-activity relationship study led to the discovery of compound C25 possessing improved enzymatic inhibitory activity and potent inhibitory activity on the proliferation of cells overexpressing PHGDH. The enzyme kinetic assay confirmed that C25 inhibited PHGDH in a nicotinamide adenine dinucleotide (NAD+) competitive manner. Molecular docking and mutagenesis experiment on PHGDH collectively revealed the binding site and key interaction residues of C25 in the PHGDH catalytic site. Taken together, this study provides information on the structural diversity for a further development of potent PHGDH inhibitors.

Considerations for the Forced Expiratory Volume in 1 Second-Based Comparative Clinical Endpoint Bioequivalence Studies for Orally Inhaled Drug Products.

Herein, we present the US Food and Drug Administration (FDA) Office of Research and Standards' current thinking, challenges, and opportunities for comparative clinical endpoint bioequivalence (BE) studies of orally inhaled drug products (OIDPs). Given the product-associated complexities of OIDPs, the FDA currently uses an aggregate weight-of-evidence approach to demonstrate that a generic OIDP is bioequivalent to its reference listed drug. The approach utilizes comparative clinical endpoint BE or pharmacodynamic BE studies, pharmacokinetic BE studies, and in vitro BE studies to demonstrate equivalence, in addition to formulation sameness and device similarity. For the comparative clinical endpoint BE studies, metrics based on forced expiratory volume in the first second (FEV1 ) are often the recommended clinical endpoints. However, the use of FEV1 can pose a challenge due to its large variability and a relatively flat dose-response relationship for most OIDPs. The utility of applying dose-scale analysis was also investigated by the FDA but often not recommended, due to either flat dose-response relationships or insufficient clinical study data. As a potential way to reduce sample size, we found adapting covariate analysis only explained a limited portion of the variation based on further investigation. The FDA continues to develop alternative methods to make BE assessment of OIDPs more cost- and time-efficient. Prospective generic drug applicants and academia are encouraged to participate in this effort by proposing new approaches in pre-abbreviated new drug application meeting requests and collaborating in the form of grants and contracts under the Generic Drug User Fee Amendments (GDUFA) Regulatory Science and Research Program.

Viscoelastic properties' characterization of corneal stromal models using non-contact surface acoustic wave optical coherence elastography (SAW-OCE).

Viscoelastic characterization of the tissue-engineered corneal stromal model is important for our understanding of the cell behaviors in the pathophysiologic altered corneal extracellular matrix (ECM). The effects of the interactions between stromal cells and different ECM characteristics on the viscoelastic properties during an 11-day culture period were explored. Collagen-based hydrogels seeded with keratocytes were used to replicate human corneal stroma. Keratocytes were seeded at 8 × 103 cells per hydrogel and with collagen concentrations of 3, 5 and 7 mg/ml. Air-pulse-based surface acoustic wave optical coherence elastography (SAW-OCE) was employed to monitor the changes in the hydrogels' dimensions and viscoelasticity over the culture period. The results showed the elastic modulus increased by 111%, 56% and 6%, and viscosity increased by 357%, 210% and 25% in the 3, 5 and 7 mg/ml hydrogels, respectively. To explain the SAW-OCE results, scanning electron microscope was also performed. The results confirmed the increase in elastic modulus and viscosity of the hydrogels, respectively, arose from increased fiber density and force-dependent unbinding of bonds between collagen fibers. This study reveals the influence of cell-matrix interactions on the viscoelastic properties of corneal stromal models and can provide quantitative guidance for mechanobiological investigations which require collagen ECM with tuneable viscoelastic properties.

Association of partial systemic exposure and abuse potential for opioid analgesics with abuse deterrence labeling claims supporting product-specific guidance.

BACKGROUND: Over the past decade, U.S. FDA has approved 10 opioid analgesics in abuse-deterrent formulations (ADFs). ADFs are intended to reduce abuse of a prescription opioid through manipulation of the product to use one or more routes of abuse. Although it is critically needed for evaluation of the abuse deterrent properties of an opioid product, the relationship between systemic exposure and likelihood of abuse of the opioid has not been fully characterized. To fill the current knowledge gap, we have evaluated the association of subjective measures predictive of abuse potential (e.g., scores of "drug liking," "take drug again"), which are referred to as 'pharmacodynamic (PD)' responses for measuring abuse potential, with systemic exposure of the opioid using the data from all the clinical abuse potential trials submitted to FDA in support of the approval of innovator ADFs. METHODS: Extensive pharmacokinetic (PK) and subjective response data from 11 clinical abuse potential trials in recreational opioid users following oral and nasal administration of intact and manipulated oxycodone, hydrocodone and morphine products from the FDA internal database were utilized for the present analysis. This retrospective study used data collected from January 11th, 2010 until March 25th, 2015. The potential relationship between PK metrics, especially those for early exposure measures, and the subjective measures of drug liking and take drug again as PD metrics of abuse potential were explored using linear and logistic regression analyses. Heterogeneity analysis was conducted to assess study-to-study variation and multi-level logistic regression analysis was used to affirm the identified PK-PD relationship based on pooled data. FINDINGS: Following oral and nasal administration of intact and manipulated opioids, the maximum visual analogue scale (VAS) for Drug Liking was generally achieved no later than the time to peak plasma drug concentration. Both heterogeneity analysis and multi-level logistic regression indicated insignificant inter study variability for the evaluated PK-PD relationships. Duration of Drug Liking response (i.e., VAS ≥ 65) lasted for 2 to 4 h after drug administration. The early portion of the systemic area under the plasma concentration-time curve (AUC), e.g., partial AUCs in the first 3 h and 4 h were found to be associated with abuse potential measures including maximum Drug Liking VAS and maximum Taking Drug Again VAS. Neither a formulation factor (e.g., immediate-release vs. extended-release, intact vs. manipulated) nor a route of administration was identified as a significant factor together with early partial AUCs to predict the probability of maximum Drug Liking or maximum Take Drug Again responses being greater than or equal to 65. INTERPRETATION: Our assessment indicates that the measure of early systemic drug exposure of opioids is the best predictor of the abuse potential response in recreational opioid users following oral or nasal administration of a single dose of an intact or manipulated abuse deterrent opioids. Our findings support FDA's recommendation of comparative PK studies with early partial AUCs as a supportive PK metric for the assessment of abuse deterrent properties of generic opioid drug products in the general and product-specific guidance's of ADFs. FUNDING: The study was partially funded by Fiscal Year 2017 Critical Path of the Center for Drug Evaluation and Research at the U.S. Food and Drug Administration.

Development of a Pediatric Relative Bioavailability/Bioequivalence Database and Identification of Putative Risk Factors Associated With Evaluation of Pediatric Oral Products.

Generally, bioequivalence (BE) studies of drug products for pediatric patients are conducted in adults due to ethical reasons. Given the lack of direct BE assessment in pediatric populations, the aim of this work is to develop a database of BE and relative bioavailability (relative BA) studies conducted in pediatric populations and to enable the identification of risk factors associated with certain drug substances or products that may lead to failed BE or different pharmacokinetic (PK) parameters in relative BA studies in pediatrics. A literature search from 1965 to 2020 was conducted in PubMed, Cochrane Library, and Google Scholar to identify BE studies conducted in pediatric populations and relative BA studies conducted in pediatric populations. Overall, 79 studies covering 37 active pharmaceutical ingredients (APIs) were included in the database: 4 bioequivalence studies with data that passed BE evaluations; 2 studies showed bioinequivalence results; 34 relative BA studies showing comparable PK parameters, and 39 relative BA studies showing differences in PK parameters between test and reference products. Based on the above studies, common putative risk factors associated with differences in relative bioavailability (DRBA) in pediatric populations include age-related absorption effects, high inter-individual variability, and poor study design. A database containing 79 clinical studies on BE or relative BA in pediatrics has been developed. Putative risk factors associated with DRBA in pediatric populations are summarized.