Michael J. Fossler

Population pharmacokinetics of tenofovir in HIV-1-uninfected members of serodiscordant couples and effect of dose reporting methods

ABSTRACT Antiretroviral preexposure prophylaxis (PrEP) with once-daily dosing of tenofovir and tenofovir-emtricitabine was shown to be effective for preventing HIV-1 infection in individuals who had HIV-1-seropositive partners (the Partners PrEP Study). We developed a population pharmacokinetic model for tenofovir and investigated the impacts of different dose reporting methods. Dosing information was collected as patient-reported dosing information (PRDI) from 404 subjects (corresponding to 1,280 drug concentration records) from the main trial and electronic monitoring-based adherence data collected from 211 subjects (corresponding to 327 drug concentration records) in an ancillary adherence study. Model development was conducted with NONMEM (7.2), using PRDI with a steady-state assumption or using PRDI replaced with electronic monitoring records where available. A two-compartment model with first-order absorption was the best model in both modeling approaches, with the need for an absorption lag time when electronic monitoring-based dosing records were included in the analysis. Age, body weight, and creatinine clearance were significant covariates on clearance, but only creatinine clearance was retained in the final models per stepwise selection. Sex was not a significant covariate on clearance. Tenofovir population pharmacokinetic parameter estimates and the precisions of the parameters from the two final models were comparable with the point estimates of the parameters, differing from 0% to 35%, and bootstrap confidence intervals widely overlapped. These findings indicate that PRDI was sufficient for population pharmacokinetic model development in this study, with a high level of adherence per multiple measures.

Effect of tamsulosin on the pharmacokinetics of dutasteride in Chinese male healthy volunteers

The purpose of this study was to evaluate the effect of tamsulosin (0.2 mg) on the pharmacokinetics of dutasteride (0.5 mg) in a group of healthy Chinese male volunteers. This was an open‐label, single‐sequence, 3‐period, drug–drug interaction phase 1 study. Twenty‐four healthy Chinese male volunteers were enrolled and administered a single dose of 0.5 mg dutasteride and, following a 28‐ to 30‐day washout period, 0.2 mg tamsulosin once daily for 7 days. On day 5, subjects received 0.2 mg tamsulosin coadministered with 0.5 mg dutasteride. Serum dutasteride and tamsulosin concentrations were monitored. In the presence or absence of tamsulosin, there were no apparent changes in dutasteride AUC and Cmax. Adverse events reported were mild to moderate in intensity and resolved by the end of the study. In healthy Chinese male volunteers, tamsulosin 0.2 mg at steady state had no apparent effect on dutasteride pharmacokinetics. Dutasteride and tamsulosin when administered alone or in combination were well tolerated.

Infusions Are the Optimal Dosing Method in Intravenous ADME Studies Rather Than Bolus Dosing

A vast majority of new drugs are developed for oral administration. However, intravenous studies are often conducted to elucidate fundamental absorption, distribution, metabolism, and excretion (ADME) properties, such as bioavailability. A common study design involves a traditional 2-way crossover with oral and intravenous dosing using doses that achieve therapeutic concentrations. More novel designs employing microdosing in a phase 0 two-way crossover or in a single arm dosing the therapeutic oral dose with a microtracer, that is, a radiolabeled intravenous microdose typically administered at the Tmax of the oral dose, are increasing in application. These microdose studies, defined as studies in which the dose is 100 μg, 1/100th the no adverse event limit, and 1/100th the pharmacologically active dose,1 are growing in popularity as the value is realized because of the potential cost savings of an exploratory investigational new drug (IND) application and selection of a candidate with acceptable pharmacokinetics for further development, compared with a full IND (0.5–0.75 vs 1.5–2.5 M).2 In addition, utilization of microdose studiesmay continue to grow as the potential applications expand and experience is gained with these applications, such as phase 0 exploration of a compound as a potential victim of drug–drug interactions or pharmacodynamic exploration. Finally, these study designs are used to fulfill the regulatory requirement from the Australian Healthy authorities to understand the absolute bioavailability.3 To highlight one recent example, 4 Nav1.7 inhibitors were explored in a phase 0 microdose study whereby 100 μg of each compound was dosed both intravenously and orally.4 Using the intravenous and in vitro ADME data, a physiologically based pharmacokinetic (PBPK) model was developed and validated using the oral data. This model was then used to predict pharmacokinetic (PK) profiles for each compound at various potential clinical doses and compare the Cmin relative to the Nav1.7 IC50, leading to the selection of the development candidate. Thorough reviews of various applications and methods of microdose and microtracer studies are provided elsewhere.2,5

Oliceridine, a Novel G Protein–Biased Ligand at the μ‐Opioid Receptor, Demonstrates a Predictable Relationship Between Plasma Concentrations and Pain Relief. II: Simulation of Potential Phase 3 Study Designs Using a Pharmacokinetic/Pharmacodynamic Model

Oliceridine is a novel G protein–biased ligand at the μ‐opioid receptor that differentially activates G protein coupling while mitigating β‐arrestin recruitment. Unlike morphine, oliceridine has no known active metabolites; therefore, analgesic efficacy is predictably linked to its concentration in the plasma. Oliceridine is primarily hepatically metabolized by CYP3A4 and CYP2D6. Using a pharmacokinetic/pharmacodynamic model relating oliceridine plasma concentrations to its effect on pain intensity as measured by numeric pain‐rating scale (NPRS) scores, we have simulated potential dosing regimens using both fixed‐dose regimens and as‐needed (prn) dosing regimens in which various doses of oliceridine were administered if NPRS scores indicated moderate to severe pain (≥4 on a 0‐10 scale). In addition, regimens in which oliceridine was self‐administered via a patient‐controlled analgesia device were also simulated. The simulated population included 10% CYP2D6 poor metabolizers (PM). The simulation results suggest that oliceridine doses of 1‐3 mg prn should be effective in reducing NPRS scores relative to placebo. The simulations also revealed that a 1‐mg “supplemental dose” given 0.25 hour after the loading dose would decrease NPRS scores further in almost one‐third of patients. In addition, if oliceridine is administered prn, a longer interval between doses is observed in simulated PM patients, consistent with their reduced oliceridine clearance. Because this longer average dosing interval is predicted to decrease oliceridine exposure in PM patients, the need to know the patients CYP2D6 genotype for dosing is effectively obviated.

Oliceridine (TRV130), a Novel G Protein–Biased Ligand at the μ‐Opioid Receptor, Demonstrates a Predictable Relationship Between Plasma Concentrations and Pain Relief. I: Development of a Pharmacokinetic/Pharmacodynamic Model

Conventional opioids bind to μ‐opioid receptors and activate 2 downstream signaling pathways: G‐protein coupling, linked to analgesia, and β‐arrestin recruitment, linked to opioid‐related adverse effects and limiting efficacy. Oliceridine (TRV130) is a novel G protein–biased ligand at the μ‐opioid receptor that differentially activates G‐protein coupling while mitigating β‐arrestin recruitment. Using data derived from both phase 1 studies in healthy volunteers as well as data from a phase 2 study examining the efficacy of oliceridine for the treatment of postbunionectomy pain, we have developed a population pharmacokinetic/pharmacodynamic model linking the pharmacokinetics of oliceridine to its effect on pain, as measured by the Numeric Pain Rating Scale score. Phase 1 data consisted of 145 subjects (88% male, 12% female), who received single doses of oliceridine ranging between 0.15 and 7 mg, as well as multiple doses ranging from 0.4 to 4.5 mg every 4–6 hours. Sixteen of these subjects were CYP2D6 poor metabolizers, who have lower oliceridine clearance than extensive metabolizers. Approximately 265 subjects (10% male, 90% female) came from the phase 2 study, in which they received active doses ranging from 0.5 to 4 mg every 3–4 hours. The final model was a 3‐compartment model that included covariates of body weight, sex, and CYP2D6 status. The PD model was an indirect response model linked to plasma oliceridine concentrations and included the placebo pain response over the 48‐hour treatment period. The EC50 for oliceridine on pain relief was estimated as 10.1 ng/mL (95%CI, 8.4–12.1 ng/mL). Model qualification showed that the model robustly reproduced the original data.

A systematic evaluation of effect of adherence patterns on the sample size and power of a clinical study

The objective of our study was to evaluate the effect of adherence patterns on the sample size and power of a clinical trial. Simulations from a population pharmacokinetic/pharmacodynamic (PK/PD) model linked to an adherence model were used. Four types of drug characteristics, such as long (~35 hours) and short (~12 hours) half‐life in combination with earlier or delayed time to reach steady‐state PD end points were studied. Adherence patterns were simulated using Markov chains. Our results clearly demonstrate the significant impact of varying levels and patterns of nonadherence on the sample size and power of a study. For drugs with short half‐lives the evidence to support efficacy could be diluted by various patterns of nonadherence that would make its efficacy indistinguishable from the response to placebo. Prospectively utilizing clinical trial simulations with thorough incorporation of various adherence patterns would provide valuable information when designing a trial.

Markov Mixed Effects Modeling Using Electronic Adherence Monitoring Records Identifies Influential Covariates to HIV Preexposure Prophylaxis

Adherence is a major factor in the effectiveness of preexposure prophylaxis (PrEP) for HIV prevention. Modeling patterns of adherence helps to identify influential covariates of different types of adherence as well as to enable clinical trial simulation so that appropriate interventions can be developed. We developed a Markov mixed‐effects model to understand the covariates influencing adherence patterns to daily oral PrEP. Electronic adherence records (date and time of medication bottle cap opening) from the Partners PrEP ancillary adherence study with a total of 1147 subjects were used. This study included once‐daily dosing regimens of placebo, oral tenofovir disoproxil fumarate (TDF), and TDF in combination with emtricitabine (FTC), administered to HIV‐uninfected members of serodiscordant couples. One‐coin and first‐ to third‐order Markov models were fit to the data using NONMEM® 7.2. Model selection criteria included objective function value (OFV), Akaike information criterion (AIC), visual predictive checks, and posterior predictive checks. Covariates were included based on forward addition (α = 0.05) and backward elimination (α = 0.001). Markov models better described the data than 1‐coin models. A third‐order Markov model gave the lowest OFV and AIC, but the simpler first‐order model was used for covariate model building because no additional benefit on prediction of target measures was observed for higher‐order models. Female sex and older age had a positive impact on adherence, whereas Sundays, sexual abstinence, and sex with a partner other than the study partner had a negative impact on adherence. Our findings suggest adherence interventions should consider the role of these factors.

Some Thoughts About the Mean Concentration‐Versus‐Time Plot

Mean plasma concentration-versus-time plots are such a staple of pharmacokinetic reports, both in the published literature and in internal industry and regulatory documents, that one seldom gives much thought to their production. After all, nothing could be more basic than plotting the mean of the plasma concentration data against time. It is generally the first thing stakeholders (ie, executives and other decision-makers) want to see when looking at study data, and often this audience (which usually does not have formal training in either pharmacokinetics or statistics) seldom goes much further into the data than this simple plot. However simple these plots may be, they are important, and in thinking about them, it seems tome that often themean plot does not quite convey what we want it to convey. So what is it that we want this plot to convey? I can think of two important pieces of information that could be conveyed by this plot:

Clinical pharmacology, creating current and future success in drug development

In response to an accelerating emergence of novel therapeutic platforms, regulatory development paradigms, and advances in analytical technology, the Clinical Pharmacology Leadership Group within the International Consortium for Innovation and Quality in Drug Development convened a Working Group to discuss these matters and formulate a vision of clinical pharmacology science for the next decade. The Working Group met throughout 2013/2014 and identified a number of critical needs and opportunities that, if addressed, will ensure that clinical pharmacology continues to provide core value to the drug development process. This Working Group did recognize prior commentaries on the state of clinical pharmacology and considered those expert opinions during the course of our discussions, such as those authored by Rawlins, Honig, and LaLonde. In contrast with these earlier commentaries, this effort intended to identify immediate and long-term opportunities and present solutions that are particularly related to drug development efforts. Over the past decade, there have been remarkable advances in the field of molecular biology that, with increased understanding of disease etiology, have resulted in the transition of increasing numbers of novel therapeutic classes into clinical development. Antibody–drug conjugates, immune therapy directed at oncology and inflammation targets, synthetic DNA engaging mRNA (antisense), proteosome modulation, and gene editing are just a few of the emerging therapeutic classes and treatment modalities that have benefited from these advances with several that have achieved approval and others either in or close to entering clinical development. Likewise, a number of nascent analytical technologies have matured into viable means for measuring drug/ metabolite concentration in the blood compartment and in some cases at the site of action. Biomarkers that demonstrate ligand:receptor interaction (target engagement) and pharmacological activity are becoming commonplace in many therapeutic areas. Although oncology has used imaging technology tomonitor target engagement andeffectwith great success, similar value is being realized in other therapeutic areas such as neurology and cardiology. The evolution of companion diagnostics has occurred in concert and will grow commensurate with our ability to differentiate both patients and disease. These advances have given us significant opportunities to move promising therapies into pivotal clinical trials with a better understanding of the likelihood of technical success and associated risks regarding safety and efficacy. The discipline of clinical pharmacology plays a pivotal role in optimizing novel therapeutic approaches while ensuring that development decisions are made based on an understanding of inherent likelihood of success and attendant risks.