Training the next generation of pharmacometric modelers: a multisector perspective.

The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970's and early 1980's and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations.

Role of Disease Progression Models in Drug Development.

The use of Disease progression models (DPMs) in Drug Development has been widely adopted across therapeutic areas as a method for integrating previously obtained disease knowledge to elucidate the impact of novel therapeutics or vaccines on disease course, thus quantifying the potential clinical benefit at different stages of drug development programs. This paper provides a brief overview of DPMs and the evolution in data types, analytic methods, and applications that have occurred in their use by Quantitive Clinical Pharmacologists. It also provides examples of how these models have informed decisions and clinical trial design across several therapeutic areas and at various stages of development. It briefly describes potential new applications of DPMs utilizing emerging data sources, and utilizing new analytic techniques, and discuss new challenges faced such as requiring description of multiple endpoints, rapid model development, application of machine learning-based analytics, and use of high dimensional and real-world data. Considerations for the continued evolution future of DPMs to serve as community-maintained expert systems are also provided.

Minimization of CYP2D6 Polymorphic Differences and Improved Bioavailability via Transdermal Administration: Latrepirdine Example.

PURPOSE: Transdermal delivery has the potential to offer improved bioavailability by circumventing first-pass gut and hepatic metabolism. This study evaluated the pharmacokinetics of oral immediate release and transdermal latrepirdine in extensive and poor CYP2D6 metabolizers (EM/PM). METHODS: Latrepirdine transdermal solution was prepared extemporaneously. The solution was applied with occlusive dressing to upper or middle back for 24 h. Each subject received a single dose of 8.14 mg oral, 5 mg transdermal, and 10 mg transdermal (EMs only) latrepirdine free base in a fixed sequence. RESULTS: Twelve EMs and 7 PMs (50-79 years) enrolled and completed the study. Latrepirdine was well tolerated following both routes of administration. Dose-normalized latrepirdine total exposures were approximately 11-fold and 1.5-fold higher in EMs and PMs, respectively following administration of transdermal relative to oral. Differences between EM and PM latrepirdine exposures were decreased, with PMs having 1.9- and 2.7-fold higher peak and total exposures, respectively, following transdermal administration compared to 11- and 20-fold higher exposures, respectively, following oral administration. CONCLUSION: Transdermal delivery can potentially mitigate the large intersubject differences observed with compounds metabolized primarily by CYP2D6. Transdermal delivery was readily accomplished in the clinic using an extemporaneously prepared solution [NCT00990613].

Improved utilization of ADAS-cog assessment data through item response theory based pharmacometric modeling.

PURPOSE: This work investigates improved utilization of ADAS-cog data (the primary outcome in Alzheimer's disease (AD) trials of mild and moderate AD) by combining pharmacometric modeling and item response theory (IRT). METHODS: A baseline IRT model characterizing the ADAS-cog was built based on data from 2,744 individuals. Pharmacometric methods were used to extend the baseline IRT model to describe longitudinal ADAS-cog scores from an 18-month clinical study with 322 patients. Sensitivity of the ADAS-cog items in different patient populations as well as the power to detect a drug effect in relation to total score based methods were assessed with the IRT based model. RESULTS: IRT analysis was able to describe both total and item level baseline ADAS-cog data. Longitudinal data were also well described. Differences in the information content of the item level components could be quantitatively characterized and ranked for mild cognitively impairment and mild AD populations. Based on clinical trial simulations with a theoretical drug effect, the IRT method demonstrated a significantly higher power to detect drug effect compared to the traditional method of analysis. CONCLUSION: A combined framework of IRT and pharmacometric modeling permits a more effective and precise analysis than total score based methods and therefore increases the value of ADAS-cog data.

Modeling and simulation for medical product development and evaluation: highlights from the FDA-C-Path-ISOP 2013 workshop.

Medical-product development has become increasingly challenging and resource-intensive. In 2004, the Food and Drug Administration (FDA) described critical challenges facing medical-product development by establishing the critical path initiative [1]. Priorities identified included the need for improved modeling and simulation tools, further emphasized in FDA's 2011 Strategic Plan for Regulatory Science [Appendix]. In an effort to support and advance model-informed medical-product development (MIMPD), the Critical Path Institute (C-Path) [www.c-path.org], FDA, and International Society of Pharmacometrics [www.go-isop.org] co-sponsored a workshop in Washington, D.C. on September 26, 2013, to examine integrated approaches to developing and applying model- MIMPD. The workshop brought together an international group of scientists from industry, academia, FDA, and the European Medicines Agency to discuss MIMPD strategies and their applications. A commentary on the proceedings of that workshop is presented here.

Patient Centric Microsampling to Support Paxlovid Clinical Development: Bridging and Implementation.

Nirmatrelvir is a potent and selective severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) main protease inhibitor. Nirmatrelvir co-packaged with ritonavir (as PAXLOVID) received US Food and Drug Administration (FDA) Emergency Use Authorization (EUA) on December 22, 2021, as an oral treatment for coronavirus disease 2019 (COVID-19) and subsequent new drug application approval on May 25, 2023. Pharmacokinetic (PK) capillary blood sampling at-home using Tasso-M20 micro-volumetric sampling device was implemented in the program, including three phase II/III outpatient and several clinical pharmacology studies supporting the EUA. The at-home sampling complemented venous blood sampling procedures to enrich the PK dataset, to decrease the need for patients' site visit for PK sampling, and to allow different sampling approaches for flexibility and convenience. To demonstrate concordance/equivalence, bridging between venous plasma and Tasso dried blood results was conducted by comparing concentrations and derived PK parameters from both sampling approaches. In addition, a two-compartment population PK model was utilized to bridge the plasma and Tasso data by estimating the PK parameters using blood-to-plasma ratio as a slope parameter. Operational challenges were successfully managed to implement at-home PK sampling in global phase II/III trials. Sample quality was generally very good with less than 3% samples deemed as "not usable" from over 800 samples collected in all the studies. Experience gained from sites and patients will guide future broader implementations.

Quantitative prediction of renal transporter-mediated clinical drug-drug interactions.

Kidney plays a critical role in the elimination of xenobiotics. Drug-drug interactions (DDIs) via inhibition of renal organic anion (OAT) and organic cation (OCT) transporters have been observed in the clinic. This study examined the quantitative predictability of renal transporter-mediated clinical DDIs based on basic and mechanistic models. In vitro transport and clinical pharmacokinetics parameters were used to quantitatively predict DDIs of victim drugs when coadministrated with OAT or OCT inhibitors, probenecid and cimetidine, respectively. The predicted changes in renal clearance (CLr) and area under the plasma concentration-time curve (AUC) were comparable to that observed in clinical studies. With probenecid, basic modeling predicted 61% cases within 25% and 94% cases within 50% of the observed CLr changes in clinic. With cimetidine, basic modeling predicted 61% cases within 25% and 92% cases within 50% of the observed CLr changes in clinic. Additionally, the mechanistic model predicted 54% cases within 25% and 92% cases within 50% of the observed AUC changes with probenecid. Notably, the magnitude of AUC changes attributable to the renal DDIs is generally less than 2-fold, unlike the DDIs associated with inhibition of CYPs and/or hepatic uptake transporters. The models were further used to evaluate the renal DDIs of Pfizer clinical candidates/drugs, and the overall predictability demonstrates their utility in the drug discovery and development settings.

Bioequivalence assessment of a pregabalin capsule and oral solution in fasted healthy volunteers: a randomized, crossover study.

OBJECTIVE: To determine the oral bioavailability of a pregabalin capsule relative to a pregabalin solution. METHODS: This was an open-label, randomized, crossover study in 12 healthy volunteers. Pharmacokinetics were compared for a 100-mg capsule and a 100-mg capsule dissolved in water, administered fasted. RESULTS: Mean Cmax and AUC0-∞ for the capsule were within 2% of those for the solution (3.8 vs. 3.7 µg/ml and 26.7 vs. 27.0 µg×h/ml, respectively). The 90% confidence intervals for the ratios of Cmax and AUC0-∞ fell fully within 80 - 125%. CONCLUSIONS: A 100-mg pregabalin capsule is bioequivalent to a pregabalin solution (100-mg capsule dissolved in water).

Fishery catch records support machine learning-based prediction of illegal fishing off US West Coast.

Illegal, unreported, and unregulated (IUU) fishing is a major problem worldwide, often made more challenging by a lack of at-sea and shoreside monitoring of commercial fishery catches. Off the US West Coast, as in many places, a primary concern for enforcement and management is whether vessels are illegally fishing in locations where they are not permitted to fish. We explored the use of supervised machine learning analysis in a partially observed fishery to identify potentially illicit behaviors when vessels did not have observers on board. We built classification models (random forest and gradient boosting ensemble tree estimators) using labeled data from nearly 10,000 fishing trips for which we had landing records (i.e., catch data) and observer data. We identified a set of variables related to catch (e.g., catch weights and species) and delivery port that could predict, with 97% accuracy, whether vessels fished in state versus federal waters. Notably, our model performances were robust to inter-annual variability in the fishery environments during recent anomalously warm years. We applied these models to nearly 60,000 unobserved landing records and identified more than 500 instances in which vessels may have illegally fished in federal waters. This project was developed at the request of fisheries enforcement investigators, and now an automated system analyzes all new unobserved landings records to identify those in need of additional investigation for potential violations. Similar approaches informed by the spatial preferences of species landed may support monitoring and enforcement efforts in any number of partially observed, or even totally unobserved, fisheries globally.

Combining patient-level and summary-level data for Alzheimer's disease modeling and simulation: a ß regression meta-analysis.

Our objective was to develop a beta regression (BR) model to describe the longitudinal progression of the 11 item Alzheimer's disease (AD) assessment scale cognitive subscale (ADAS-cog) in AD patients in both natural history and randomized clinical trial settings, utilizing both individual patient and summary level literature data. Patient data from the coalition against major diseases database (3,223 patients), the Alzheimer's disease neruroimaging initiative study database (186 patients), and summary data from 73 literature references (representing 17,235 patients) were fit to a BR drug-disease-trial model. Treatment effects for currently available acetyl cholinesterase inhibitors, longitudinal changes in disease severity, dropout rate, placebo effect, and factors influencing these parameters were estimated in the model. Based on predictive checks and external validation, an adequate BR meta-analysis model for ADAS-cog using both summary-level and patient-level data was developed. Baseline ADAS-cog was estimated from baseline MMSE score. Disease progression was dependent on time, ApoE4 status, age, and gender. Study drop out was a function of time, baseline age, and baseline MMSE. The use of the BR constrained simulations to the 0-70 range of the ADAS-cog, even when residuals were incorporated. The model allows for simultaneous fitting of summary and patient level data, allowing for integration of all information available. A further advantage of the BR model is that it constrains values to the range of the original instrument for simulation purposes, in contrast to methodologies that provide appropriate constraints only for conditional expectations.

Ensuring diversity in clinical trials: The role of clinical pharmacology.

Increasing the diversity of participants in clinical trials is important as it allows further examination of drug effects in all subgroups of patients who will be prescribed an approved medicine. It also gives patients more confidence in the medicine when they know that individuals similar to themselves have participated in pivotal efficacy and safety trials. Pfizer recently committed to ensuring that its clinical trials reflect racial and ethnic demographics of the patient populations in the countries and communities in which the trials are conducted. This paper furthers Pfizer's commitment by declaring what Clinical Pharmacology (CP) can do to advance this goal and expand patient populations to include other groups such as pediatrics, elderly, and those with organ impairment. This includes steps such as: Pfizer Clinical Pharmacology commits to these actions, which create a framework for the CP Community to enable increased diversity among participants in clinical trials and improved dosing recommendations for all patient subgroups.

Pharmacokinetics of Oral Nirmatrelvir/Ritonavir, a Protease Inhibitor for Treatment of COVID-19, in Subjects With Renal Impairment.

Nirmatrelvir coadministered with ritonavir is highly efficacious in reducing the risk of coronavirus disease 2019 (COVID-19) adverse outcomes among patients at increased risk of progression to severe disease, including patients with chronic kidney disease. Because nirmatrelvir is eliminated by the kidneys when given with ritonavir, this phase I study evaluated the effects of renal impairment on pharmacokinetics, safety, and tolerability of nirmatrelvir/ritonavir. Participants with normal renal function (n = 10) or mild, moderate, or severe renal impairment (n = 8 each) were administered a single 100-mg nirmatrelvir dose with 100 mg ritonavir given 12 hours before, together with and 12 and 24 hours after the nirmatrelvir dose. Systemic nirmatrelvir exposure increased with increasing renal impairment, with mild, moderate, and severe renal impairment groups having respective adjusted geometric mean ratio areas under the plasma concentration-time profile from time 0 extrapolated to infinite time of 124%, 187%, and 304% vs. the normal renal function group. Corresponding ratios for maximum plasma concentration were 130%, 138%, and 148%. Apparent clearance was positively correlated with estimated glomerular filtration rate, and geometric mean renal clearance values were particularly lower for the moderate (47% decrease) and severe (80% decrease) renal impairment groups vs. the normal renal function group. Nirmatrelvir/ritonavir exhibited an acceptable safety profile; treatment-related adverse events were mild in severity, and there were no significant findings regarding laboratory measurements, vital signs, or electrocardiogram assessments. These findings led to a dose reduction recommendation for nirmatrelvir/ritonavir in patients with moderate renal impairment (150/100 mg nirmatrelvir/ritonavir instead of 300/100 mg twice daily for 5 days). NCT04909853.

Population pharmacokinetics of pregabalin in healthy subjects and patients with chronic pain or partial seizures.

PURPOSE: Pregabalin, a high-affinity ligand for α2δ subunits of voltage-gated calcium channels, is a novel pharmacotherapy for chronic pain, partial seizures, and other disorders. The present study investigated the population pharmacokinetics of pregabalin following single and multiple doses in healthy volunteers and patient populations. METHODS: Using nonlinear mixed-effect modeling, 5,583 plasma pregabalin concentration-time samples from 1,723 subjects were analyzed: 2,868 samples from healthy volunteers or subjects with renal impairment (n = 123), 1,513 from patients with partial seizures (n = 626), and 1,202 from patients with chronic pain (n = 974). A one-compartment model with first-order elimination and absorption processes and absorption lag time was used. KEY FINDINGS: This pharmacostatistical model showed that: (1) pregabalin oral clearance (CL/F) was directly proportional to creatinine clearance (CLcr), but was independent of gender, race, age, female hormonal status, daily dose, and dosing regimen; (2) apparent volume of distribution was dependent on body weight and gender; (3) absorption rate was decreased when given with food; and (4) coadministration with marketed antiepileptic drugs (AEDs) had no significant effect on pregabalin CL/F. SIGNIFICANCE: Pregabalin CL/F is related to CLcr, and this relationship is similar among healthy volunteers and patients with either partial seizures or chronic pain disorders. The only factor having a clinically significant influence on steady-state plasma pregabalin concentrations is renal function.

Modeling sleep data for a new drug in development using markov mixed-effects models.

PURPOSE: To characterize the time-course of sleep in insomnia patients as well as placebo and concentration-effect relationships of two hypnotic compounds, PD 0200390 and zolpidem, using an accelerated model-building strategy based on mixed-effects Markov models. METHODS: Data were obtained in a phase II study with the drugs. Sleep stages were recorded during eight hours of sleep for two nights per treatment for the five treatments. First-order Markov models were developed for one transition at a time in a sequential manner; first a baseline model, followed by placebo and lastly the drug models. To accelerate the process, predefined models were selected based on a priori knowledge of sleep, including inter-subject and inter-occasion variability. RESULTS: Baseline sleep was described using piece-wise linear models, depending on time of night and duration of sleep stage. Placebo affected light sleep stages; drugs also affected slow-wave sleep. Administering PD 0200390 30 min earlier than standard dosing was shown through simulations to reduce latency to persistent sleep by 40%. CONCLUSION: The proposed accelerated model-building strategy resulted in a model well describing sleep patterns of insomnia patients with and without treatments.

Disease progression model for cognitive deterioration from Alzheimer's Disease Neuroimaging Initiative database.

BACKGROUND: A mathematical model was developed to describe the longitudinal response in Alzheimer's Disease Assessment Scale-cognitive (ADAS-cog) obtained from the Alzheimer's Disease Neuroimaging Initiative. METHODS: The model was fit to the longitudinal ADAS-cog scores from 817 patients. Risk factors (age, apolipoprotein ɛ4 [APOE ɛ4] genotype, gender, family history of AD, years of education) and baseline severity were tested as covariates. RESULTS: Rate of disease progression increased with baseline severity. Age, APOE ɛ4 genotype, and gender were identified as potential covariates influencing disease progression. The rate of disease progression in patients with mild to moderate AD was estimated as approximately 5.5 points/yr. CONCLUSIONS: A disease progression model adequately described the natural decline of ADAS-cog observed in Alzheimer's Disease Neuroimaging Initiative. Baseline severity is an important covariate to predict a curvilinear rate of disease progression in normal elderly, mild cognitive impairment, and AD patients. Age, APOE ɛ4 genotype, and gender also influence the rate of disease progression.

Demographic diversity of participants in Pfizer sponsored clinical trials in the United States.

The approval of new medicinal agents requires robust efficacy and safety clinical trial data demonstrated to be applicable to population subgroups. Limited data have previously been reported by drug sponsors on the topic of clinical trial diversity. In order to establish a baseline of diversity in our clinical trials that can be used by us and other sponsors, an analysis of clinical trial diversity was conducted covering race, ethnicity, sex, and age. This analysis includes Pfizer interventional clinical trials that initiated enrollment between 2011 through 2020. The data set comprises 213 trials with 103,103 US participants. The analysis demonstrated that overall trial participation of Black or African American individuals was at the US census level (14.3% vs 13.4%), participation of Hispanic or Latino individuals was below US census (15.9% vs 18.5%), and female participation was at US census (51.1% vs 50.8%). The analysis also examined the percentage of trials that achieved racial and ethnic distribution levels at or above census levels. Participant levels above census were achieved in 56.1% of Pfizer trials for Black or African American participants, 51.4% of trials for White participants, 16.0% of trials for Asian participants, 14.2% of trials for Native Hawaiian and Pacific Islander participants, 8.5% of trials for American Indian and Alaska Native participants, and 52.3% of trials for Hispanic or Latino participants. The results presented here provide a baseline upon which we can quantify the impact of our ongoing efforts to improve racial and ethnic diversity in clinical trials.

Disease progression meta-analysis model in Alzheimer's disease.

BACKGROUND: Various authors have evaluated disease progression in Alzheimer's disease (AD), using patient data from individual clinical studies or pooled data across various trials. We conducted a systematic review of public data sources from 1990 to 2008 for all available AChE inhibitor studies, as well as clinical studies that evaluated the rate of deterioration in AD patients. Unique to this analysis, we developed a model based on literature data to describe the longitudinal response in the Alzheimer's Disease Assessment Scale-Cognitive (ADAS-cog) (change from baseline) in mild to moderate severity AD patients. The model was used to estimate disease progression for both placebo-treated patients and acetylcholinesterase (AChE)-inhibitor treated patients, and factors that affected disease progression. METHODS: We collected 576 mean ADAS-cog changes from baseline data points of 52 trials, representing data from approximately 19,972 patients and more than 84,000 individual observations. The model described the rate of disease progression, the evident placebo effect, and the symptomatic effect of AChE-inhibitors. Baseline ADAS-cog, Mini-Mental State Examination score, age, and year of publication were tested as covariates. RESULTS: The disease progression in mild to moderate AD patients across all available and relevant literature sources was estimated as 5.5 points per year. An Emax-type model best described the symptomatic drug effect of AChE inhibitors. The rate of disease progression (underlying disease progression) was no different between placebo and AChE-inhibitors groups. Baseline ADAS-cog is a significant covariate in disease progression. Baseline age was also tested as a covariate in the rate of disease progression, but the model was unable to describe any effects of age, likely because of the narrow distribution of mean age (literature-level analysis). There was no significant impact of publication year in the model. CONCLUSIONS: Baseline ADAS-cog is a significant covariate affecting the rate of disease progression, and it describes or at least explains the different rates of deterioration evident in early or late stages of the disease. There was no significant impact of publication year in the model, suggesting that disease progression has not slowed in more recent trials.