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.

pyDarwin machine learning algorithms application and comparison in nonlinear mixed-effect model selection and optimization.

Forward addition/backward elimination (FABE) has been the standard for population pharmacokinetic model selection (PPK) since NONMEM® was introduced. We investigated five machine learning (ML) algorithms (Genetic algorithm [GA], Gaussian process [GP], random forest [RF], gradient boosted random tree [GBRT], and particle swarm optimization [PSO]) as alternatives to FABE. These algorithms were applied to PPK model selection with a focus on comparing the efficiency and robustness of each of them. All machine learning algorithms included the combination of ML algorithms with a local downhill search. The local downhill search consisted of systematically changing one or two "features" at a time (a one-bit or a two-bit local search), alternating with the ML methods. An exhaustive search (all possible combinations of model features, N = 1,572,864 models) was the gold standard for robustness, and the number of models examined leading prior to identification of the final model was the metric for efficiency.All algorithms identified the optimal model when combined with the two-bit local downhill search. GA, RF, GBRT, and GP identified the optimal model with only a one-bit local search. PSO required the two-bit local downhill search. In our analysis, GP was the most efficient algorithm as measured by the number of models examined prior to finding the optimal (495 models), and PSO exhibited the least efficiency, requiring 1710 unique models before finding the best solution. Additionally, GP was also the algorithm that needed the longest elapsed time of 2975.6 min, in comparison with GA, which only required 321.8 min.

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.

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.

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.

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.

Development of a Predictive Monte Carlo Radiative Transfer Model for Ablative Fractional Skin Lasers.

It is possible to enhance topical drug delivery by pretreatment of the skin with ablative fractional lasers (AFLs). However, the parameters to use for a given AFL to achieve the desired depth of ablation or the desired therapeutic or cosmetic outcome are hard to predict. This leaves open the real possibility of overapplication or underapplication of laser energy to the skin. In this study, we developed a numerical model consisting of a Monte Carlo radiative transfer (MCRT) code coupled to a heat transfer and tissue damage algorithm. The simulation is designed to predict the depth effects of AFL on the skin, verified with in vitro experiments in porcine skin via optical coherence tomography (OCT) imaging. Ex vivo porcine skin is irradiated with increasing energies (50-400 mJ/pixel) from a CO2 AFL. The depth of microscopic treatment zones is measured and compared with our numerical model. The data from the OCT images and MCRT model complement each other well. Nonablative thermal effects on surrounding tissue are also discussed. This model, therefore, provides an initial step toward a predictive determination of the effects of AFL on the skin. Lasers Surg. Med. © 2020 The Authors. Lasers in Surgery and Medicine published by Wiley Periodicals LLC.

Modeling epidemic spreading through public transit using time-varying encounter network.

Passenger contact in public transit (PT) networks can be a key mediate in the spreading of infectious diseases. This paper proposes a time-varying weighted PT encounter network to model the spreading of infectious diseases through the PT systems. Social activity contacts at both local and global levels are also considered. We select the epidemiological characteristics of coronavirus disease 2019 (COVID-19) as a case study along with smart card data from Singapore to illustrate the model at the metropolitan level. A scalable and lightweight theoretical framework is derived to capture the time-varying and heterogeneous network structures, which enables to solve the problem at the whole population level with low computational costs. Different control policies from both the public health side and the transportation side are evaluated. We find that people's preventative behavior is one of the most effective measures to control the spreading of epidemics. From the transportation side, partial closure of bus routes helps to slow down but cannot fully contain the spreading of epidemics. Identifying "influential passengers" using the smart card data and isolating them at an early stage can also effectively reduce the epidemic spreading.

Identification of novel STAT3 inhibitors bearing 2-acetyl-7-phenylamino benzofuran scaffold for antitumour study.

Signal transducer and activator of transcription 3 (STAT3) is identified as a promising target for multiple cancer therapy and attracts widespread concern. Herein, we reported the discovery of a series of 2-acetyl-7-phenylamino benzofuran derivatives as STAT3 inhibitors using scaffold fusion strategy. Further structure activity relationship study led to the discovery of compound C6, which displayed the most potent anti-proliferation activities against MDA-MB-468 cells (IC50 = 0.16 µM). Western blot assay demonstrated that C6 inhibited the activation of STAT3 (Tyr705) without influencing the phosphorylation of STAT1 (Tyr701). Further mechanistic studies indicated that C6 caused a notable G2/M cycle-arresting and early apoptosis in a concentration-dependent manner in MDA-MB-468 cells. Finally, molecular modelling study elucidated the binding mode of C6 in STAT3 SH2 domain.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.