Physiologically-Based Pharmacokinetic Modeling to Support the Clinical Management of Drug-Drug Interactions With Bictegravir

Bictegravir is equally metabolized by cytochrome P450 (CYP)3A and uridine diphosphate-glucuronosyltransferase (UGT)1A1. Drug-drug interaction (DDI) studies were only conducted for strong inhibitors and inducers, leading to some uncertainty whether moderate perpetrators or multiple drug associations can be safely coadministered with bictegravir. We used physiologically-based pharmacokinetic (PBPK) modeling to simulate DDI magnitudes of various scenarios to guide the clinical DDI management of bictegravir. Clinically observed DDI data for bictegravir coadministered with voriconazole, darunavir/cobicistat, atazanavir/cobicistat, and rifampicin were predicted within the 95% confidence interval of the PBPK model simulations. The area under the curve (AUC) ratio of the DDI divided by the control scenario was always predicted within 1.25-fold of the clinically observed data, demonstrating the predictive capability of the used modeling approach. After the successful verification, various DDI scenarios with drug pairs and multiple concomitant drugs were simulated to analyze their effect on bictegravir exposure. Generally, our simulation results suggest that bictegravir should not be coadministered with strong CYP3A and UGT1A1 inhibitors and inducers (e.g., atazanavir, nilotinib, and rifampicin), but based on the present modeling results, bictegravir could be administered with moderate dual perpetrators (e.g., efavirenz). Importantly, the inducing effect of rifampicin on bictegravir was predicted to be reversed with the concomitant administration of a strong inhibitor such as ritonavir, resulting in a DDI magnitude within the efficacy and safety margin for bictegravir (0.5-2.4-fold). In conclusion, the PBPK modeling strategy can effectively be used to guide the clinical management of DDIs for novel drugs with limited clinical experience, such as bictegravir

Impact of Obesity on the Drug-Drug Interaction Between Dolutegravir and Rifampicin or Any Other Strong Inducers

BACKGROUND: Obesity is increasingly prevalent among people with HIV. Obesity can impact drug pharmacokinetics and consequently the magnitude of drug-drug interactions (DDIs) and, thus, the related recommendations for dose adjustment. Virtual clinical DDI studies were conducted using physiologically based pharmacokinetic (PBPK) modeling to compare the magnitude of the DDI between dolutegravir and rifampicin in nonobese, obese, and morbidly obese individuals. METHODS: Each DDI scenario included a cohort of virtual individuals (50% female) between 20 and 50 years of age. Drug models for dolutegravir and rifampicin were verified against clinical observed data. The verified models were used to simulate the concurrent administration of rifampicin (600 mg) at steady state with dolutegravir (50 mg) administered twice daily in normal-weight (BMI 18.5-30 kg/m(2)), obese (BMI 30-40 kg/m(2)), and morbidly obese (BMI 40-50 kg/m(2)) individuals. RESULTS: Rifampicin was predicted to decrease dolutegravir area under the curve (AUC) by 72% in obese and 77% in morbidly obese vs 68% in nonobese individuals; however, dolutegravir trough concentrations were reduced to a similar extent (83% and 85% vs 85%). Twice-daily dolutegravir with rifampicin resulted in trough concentrations always above the protein-adjusted 90% inhibitory concentration for all BMI groups and above the 300 ng/mL threshold in a similar proportion for all BMI groups. CONCLUSIONS: The combined effect of obesity and induction by rifampicin was predicted to further decrease dolutegravir exposure but not the minimal concentration at the end of the dosing interval. Thus, dolutegravir 50 mg twice daily with rifampicin can be used in individuals with a high BMI up to 50 kg/m(2)

Negotiating Value: Comparing Human and Animal Fracture Care in Industrial Societies

At the beginning of the twentieth-century, human and veterinary surgeons faced the challenge of a medical marketplace transformed by technology. The socio-economic value ascribed to their patients – people and domestic animals – was changing, reflecting the increasing mechanisation of industry and the decreasing dependence of society upon non-human animals for labour. In human medicine, concern for the economic consequences of fractures “pathologised” any significant level of post-therapeutic disability, a productivist perspective contrary to the traditional corpus of medical values. In contrast, veterinarians adapted to the mechanisation of horse-power by shifting their primary professional interest to companion animals; a type of veterinary patient generally valued for the unique emotional attachment of the owner, and not the productive capacity of the animal. The economic rationalisation of human fracture care and the “sentimental” transformation of veterinary orthopaedic expertise indicates how these specialists utilised increasingly convergent rhetorical arguments to justify the application of innovative fracture care technologies to their humans and animal patients. Keywords: Fracture care, Industrialisation, Veterinary History, Human/animal relation

Economic analysis of understanding and implementing design criteria for acoustic suppression in military residential units

This thesis examined and analyzed the Navy Military Housing acoustical design practices and procedures for military residential housing. The Uniform Building Code and Naval Facilities Engineering Command (NAVFACENGCOM) instruction 11101.85 were used as base line guidance for design and construction of Navy Family Housing Projects. NAVFACTENGCOM's design process was first examined to determine if more emphasis should be placed on noise suppression in Navy Family Housing. Based on the analysis, it was determined that the Navy Family Housing Program does address the design for noise suppression through the use of pre-established and factory tested Sound Transmission Class (STC) assemblies. However more emphasis should be placed on the acoustic evaluation process after a contractors' design is received for evaluation.http://archive.org/details/economicanalysis00stadLieutenant, United States NavyApproved for public release; distribution is unlimited

Sex-related pharmacokinetic differences with aging

PURPOSE: The proportion of women increases with advanced age, but older women are often underrepresented in clinical trials. Therefore, little is known about the combined effect of sex- and age-related physiological changes on drug pharmacokinetics. METHODS: We compiled clinical studies, which investigated sex-related pharmacokinetic differences both in older and young women and men. The ratio women/men was calculated for various pharmacokinetic parameters across adulthood to assess sex-related differences in drug pharmacokinetics with aging. The contribution of body weight and drug characteristics to sex-related pharmacokinetic differences were explored using analysis of variance. RESULTS: We found 67 studies reporting the pharmacokinetics for 56 drugs both in older and young women and men. Median peak concentration (C(max)) (interquartile range (IQR)) and drug exposure (AUC) (IQR) were 22% (8-41%) and 20% (0-39%) higher in women compared with men whereas time to peak concentration (t(max)), apparent volume of distribution (VdF) and elimination half-life (t(1/2)) were not significantly different. Body weight and the drug main elimination pathway contributed to sex-related differences in C(max) and AUC. Relative to men, women had a modest increase in C(max) with increasing age (r = 0.19, p = 0.04). Conversely, sex-related differences in AUC remained constant with increasing age. CONCLUSION: The pharmacokinetic differences between women and men were modest and, with the exception of C(max), remained constant with increasing age. The higher plasma concentration might be correlated to more adverse events in older women and thus, drug treatment should be started on the lower recommended dosage when appropriate particularly for drugs characterized by a narrow therapeutic index

Physiologically Based Pharmacokinetic Modelling to Identify Physiological and Drug Parameters Driving Pharmacokinetics in Obese Individuals

BACKGROUND: Obese individuals are often underrepresented in clinical trials, leading to a lack of dosing guidance. OBJECTIVE: This study aimed to investigate which physiological parameters and drug properties determine drug disposition changes in obese using our physiologically based pharmacokinetic (PBPK) framework, informed with obese population characteristics. METHODS: Simulations were performed for ten drugs with clinical data in obese (i.e., midazolam, triazolam, caffeine, chlorzoxazone, acetaminophen, lorazepam, propranolol, amikacin, tobramycin, and glimepiride). PBPK drug models were developed and verified first against clinical data in non-obese (body mass index (BMI) </= 30 kg/m(2)) and subsequently in obese (BMI >/= 30 kg/m(2)) without changing any drug parameters. Additionally, the PBPK model was used to study the effect of obesity on the pharmacokinetic parameters by simulating drug disposition across BMI, starting from 20 up to 60 kg/m(2). RESULTS: Predicted pharmacokinetic parameters were within 1.25-fold (71.5%), 1.5-fold (21.5%) and twofold (7%) of clinical data. On average, clearance increased by 1.6% per BMI unit up to 64% for a BMI of 60 kg/m(2), which was explained by the increased hepatic and renal blood flows. Volume of distribution increased for all drugs up to threefold for a BMI of 60 kg/m(2); this change was driven by pK(a) for ionized drugs and logP for neutral and unionized drugs. C(max) decreased similarly across all drugs while t(max) remained unchanged. CONCLUSION: Both physiological changes and drug properties impact drug pharmacokinetics in obese subjects. Clearance increases due to enhanced hepatic and renal blood flows. Volume of distribution is higher for all drugs, with differences among drugs depending on their pK(a)/logP

A Comprehensive Framework for Physiologically-Based Pharmacokinetic Modeling in Matlab

Physiologically-based pharmacokinetic (PBPK) models are useful tools to predict clinical scenarios for special populations for whom there are high hurdles to conduct clinical trials such as children or the elderly. However, the coding of a PBPK model in a mathematical programming language can be challenging. This tutorial illustrates how to build a whole-body PBPK model in Matlab to answer specific pharmacological questions involving drug disposition and magnitudes of drug-drug interactions in different patient populations

Repository Describing the Anatomical, Physiological, and Biological Changes in an Obese Population to Inform Physiologically Based Pharmacokinetic Models

BACKGROUND: Obesity is associated with physiological changes that can affect drug pharmacokinetics. Obese individuals are underrepresented in clinical trials, leading to a lack of evidence-based dosing recommendations for many drugs. Physiologically based pharmacokinetic (PBPK) modelling can overcome this limitation but necessitates a detailed description of the population characteristics under investigation. OBJECTIVE: The purpose of this study was to develop and verify a repository of the current anatomical, physiological, and biological data of obese individuals, including population variability, to inform a PBPK framework. METHODS: A systematic literature search was performed to collate anatomical, physiological, and biological parameters for obese individuals. Multiple regression analyses were used to derive mathematical equations describing the continuous effect of body mass index (BMI) within the range 18.5-60 kg/m(2) on system parameters. RESULTS: In total, 209 studies were included in the database. The literature reported mostly BMI-related changes in organ weight, whereas data on blood flow and biological parameters (i.e. enzyme abundance) were sparse, and hence physiologically plausible assumptions were made when needed. The developed obese population was implemented in Matlab((R)) and the predicted system parameters obtained from 1000 virtual individuals were in agreement with observed data from an independent validation obese population. Our analysis indicates that a threefold increase in BMI, from 20 to 60 kg/m(2), leads to an increase in cardiac output (50%), liver weight (100%), kidney weight (60%), both the kidney and liver absolute blood flows (50%), and in total adipose blood flow (160%). CONCLUSION: The developed repository provides an updated description of a population with a BMI from 18.5 to 60 kg/m(2) using continuous physiological changes and their variability for each system parameter. It is a tool that can be implemented in PBPK models to simulate drug pharmacokinetics in obese individuals