J Pharmacokinet Pharmacodyn [JOURNAL]
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200 papers
Exposure-safety Markov modeling of ocular adverse events in patient populations treated with tisotumab vedotin.
Feng S, Gunawan R, Passey C … +8 more , Voellinger J, Polhamus D, Gerritsen A, O'Day C, Carret AS, Soumaoro I, Gupta M, Hanley WD
J Pharmacokinet Pharmacodyn · 2025 Oct · PMID 41044356 · Full text
Tisotumab vedotin (TV), a tissue factor-directed antibody-drug conjugate (ADC), is approved in the US at 2.0 mg/kg every 3 weeks (Q3W) for adult patients with recurrent or metastatic cervical cancer following disease pro...
Tisotumab vedotin (TV), a tissue factor-directed antibody-drug conjugate (ADC), is approved in the US at 2.0 mg/kg every 3 weeks (Q3W) for adult patients with recurrent or metastatic cervical cancer following disease progression on or after chemotherapy. Previous logistic regression analysis showed a positive association between TV exposure and ocular adverse events (OAEs), which were identified as prespecified AEs of interest in TV clinical studies. To further optimize TV dose from a safety perspective, we developed a discrete-time Markov model (DTMM) to characterize exposure-response (E-R) relationships of exposures of both ADC and the microtubule-disrupting agent monomethyl auristatin E to the incidence, severity, and longitudinal time course of grade ≥ 2 OAEs in patients with advanced solid tumors. A total of 757 patients who received TV as monotherapy or combination (with carboplatin, bevacizumab, or pembrolizumab) across seven clinical studies were included in this analysis. Of multiple covariates modeled, implementation of an eye care plan was the only covariate to significantly reduce risk of grade ≥ 2 OAEs. The DTMM suggested an association between ADC exposure and risk of grade ≥ 2 OAEs. Based on the totality of data from clinical outcomes, pharmacokinetics, and E-R analyses, as well as DTMM modeling results, TV 1.7 mg/kg every 2 weeks may provide higher efficacy with slightly increased risk of OAEs compared with 2.0 mg/kg Q3W, although these OAEs are manageable with an appropriate eye care plan. ClinicalTrials.gov ID (first submission): NCT03485209 (2018-03-08), NCT03657043 (2018-08-22), NCT03438396 (2018-02-08), NCT03786081 (2018-12-13), NCT03913741 (2019-03-29), NCT02001623 (2013-11-14), and NCT02552121 (2015-09-14).
Impact of covariate model building methods on their clinical relevance evaluation in population pharmacokinetic analyses: comparison of the full model, stepwise covariate model (SCM) and SCM+ approaches - further results based on more conventional practices.
Philipp M, Buatois S, Retout S … +1 more , Mentré F
J Pharmacokinet Pharmacodyn · 2025 Sep · PMID 41015631 · Publisher ↗
Covariate clinical relevance (CCR) is commonly assessed in population pharmacokinetics using forest plots visualizing parameter changes across covariate values. In our previous work (Philipp et al. 2024), CCR was evaluat...
Covariate clinical relevance (CCR) is commonly assessed in population pharmacokinetics using forest plots visualizing parameter changes across covariate values. In our previous work (Philipp et al. 2024), CCR was evaluated using a [0.80-1.20] reference area and a 90% confidence interval for both relevance and significance assessment. However, more conventional thresholds include a broader reference area of [0.80-1.25] and the use of a 5% type I error to assess statistical significance. This commentary extends our previous analysis by evaluating CCR decisions under these more conventional thresholds, in order to assess whether the full model, the stepwise covariate modeling (SCM) and its enhanced version SCM+ remain robust. A comparison with the previous results is also provided. The revised CCR evaluation gave satisfactory results across all three approaches. For covariates with a simulated effect, the full model and SCM/SCM+ provided consistent conclusions with those of the true model. For covariates without a simulated effect, the full model mainly found them non-relevant (NR) non-significant or insufficient information (II) non-significant, while SCM/SCM+ mainly did not select them. These results align with our previous findings. Conclusions for covariates with a simulated effect were almost unchanged. For covariates without a simulated effect, the more conventional threshold allowed the full model to conclude more frequently to their NR instead of II, likely due to the broader reference area and stricter type I error control. Overall, the consistency of our results across different thresholds demonstrates their robustness and supports their generalizability.
ADPO: automatic-differentiation-assisted parametric optimization.
Chen R, Sale M, Mazur A … +6 more , Tomashevskiy M, Hu S, Craig J, Dunlavey M, Leary R, Nieforth K
J Pharmacokinet Pharmacodyn · 2025 Sep · PMID 40983828 · Publisher ↗
Automatic differentiation (AD), a key method for accurately and efficiently computing derivatives in modern machine learning, is now implemented in Phoenix® NLME™ 8.6 for the first time and applied to the first-order con...
Automatic differentiation (AD), a key method for accurately and efficiently computing derivatives in modern machine learning, is now implemented in Phoenix® NLME™ 8.6 for the first time and applied to the first-order conditional estimation extended least squares (FOCE ELS), Laplacian, and adaptive Gaussian quadrature (AGQ) algorithms. We name the AD implementation as 'automatic-differentiation-assisted parametric optimization' (ADPO), which can be enabled by checking the 'Fast Optimization' option. We present in detail how ADPO is implemented in the frequently used FOCE ELS algorithm, and analyze its performance from the benchmarks based on four PK/PD models. We show both ADPO and traditional FOCE ELS which uses gradients obtained from finite difference (FD) are reasonably accurate and robust, while the main advantage of ADPO being that it considerably reduces computation time no matter what ODE solvers are used: in general ADPO reduces the total run time by around 20% to 50% compared to traditional FOCE ELS. In a case for the realistic voriconazole model using 'auto-detect' ODE solver, 95% reduction in the total run time is observed.
Advancing drug development with "Fit-for-Purpose" modeling informed approaches.
Sheng J, Zhang T
J Pharmacokinet Pharmacodyn · 2025 Sep · PMID 40952534 · Full text
Model-informed Drug Development (MIDD) is an essential framework for advancing drug development and supporting regulatory decision-making. The current review presents a strategic blueprint to closely align MIDD tools wit...
Model-informed Drug Development (MIDD) is an essential framework for advancing drug development and supporting regulatory decision-making. The current review presents a strategic blueprint to closely align MIDD tools with key questions of interests (QOI), content of use (COU), and model impact across stages of development -from early discovery to post-market lifecycle management. To demonstrate how the strategy works, we have also provided examples of how the MIDD tools can be applied to enhance the target identification, assist with lead compound optimization, improve preclinical prediction accuracy, facilitate First-in-Human (FIH) studies, optimize clinical trial design including dosage optimization, describe clinical population pharmacokinetics/exposure-response (PPK/ER) characteristics, and support label updates during post-approval stages. Additionally, the roles of some commonly used modeling methodologies, such as quantitative structure-activity relationship (QSAR), physiologically based pharmacokinetic (PBPK), semi-mechanistic pharmacokinetics/pharmacodynamics (PK/PD), PPK/ER, and quantitative systems pharmacology (QSP), are highlighted. What is more, we also explored the evolving role of MIDD in the context of emerging technologies, such as artificial intelligence (AI) and machine learning (ML) approaches. Further, MIDD utilities in development and regulatory evaluation of 505(b) (2) and generic drug products, as well as practical considerations of MIDD in regulatory interactions and asset acquisitions, are briefly discussed. At the end of the review, we briefly addressed the potential challenges faced by MIDD, such as lack of appropriate resources and slow organizational acceptance and alignment, as well as our perspectives on future opportunities of how MIDD could be further expanded.
Computational approaches for toxicology and Pharmacokinetic properties prediction.
Kaboudi N, Shekari T, Shayanfar A … +1 more , Pimentel AS
J Pharmacokinet Pharmacodyn · 2025 Sep · PMID 40908375 · Publisher ↗
Pharmacokinetics and toxicological studies how the body reacts to a specific administered substance, such as a drug, toxin, or food. Each substance experiences these four steps: absorption, distribution, metabolism, and...
Pharmacokinetics and toxicological studies how the body reacts to a specific administered substance, such as a drug, toxin, or food. Each substance experiences these four steps: absorption, distribution, metabolism, and excretion, which are the main parameters in pharmacokinetics studies. Many toxic endpoints exist. There are three main ways to measure toxicology and pharmacokinetic parameters: in vivo, in vitro, and in-silico. Knowing toxicological and pharmacokinetic parameters before developing a new drug candidate could save time and resources, as clinical studies are highly cost-demanding. This review aims to gather studies using in-silico methodologies to predict pharmacokinetic properties.
Concentration-dependent blood binding: assessing implications through physiologically based Pharmacokinetic modeling of tacrolimus as a case example.
El-Khateeb E, Karsanji D, Darwich AS … +1 more , Rostami-Hodjegan A
J Pharmacokinet Pharmacodyn · 2025 Sep · PMID 40908372 · Publisher ↗
Concentration-dependent binding to red blood cells is a characteristic of several drugs, complicating the understanding of how pathophysiological factors influence drug behavior. This study utilized user-friendly, physio...
Concentration-dependent binding to red blood cells is a characteristic of several drugs, complicating the understanding of how pathophysiological factors influence drug behavior. This study utilized user-friendly, physiologically-based pharmacokinetic (PBPK) models to compare concentration-dependent and independent blood-to-plasma drug concentration ratios (B/P), using tacrolimus as a case study. Two models were developed and validated for tacrolimus using clinical data from healthy volunteers; Model 1 accounted for saturable blood binding, and Model 2 used a constant B/P level. The differences between the two models based on the two binding assumptions were also studied across clinically relevant hematocrit (HCT) and dose levels. For intravenous (IV) infusions, varying HCT from 15 to 45% resulted in a predicted difference in the area under the concentration-time curve (AUC) of 6-9% for total drug concentration in blood and 37-39% for unbound drug concentration in plasma. Increasing IV doses increased the predicted differences in blood AUC. For oral dosing to steady state, predicted differences in trough concentrations ranged between 50% and 130%, peak concentrations (78-284%), and AUC (up to 125%) according to HCT, dose, and biological medium, e.g., trough differences ranged from 50% (blood, 5 mg) to 130% (plasma, 10 mg). A hypothetical scenario of tacrolimus dose levels increasing above clinically relevant doses revealed a reducing difference in outcomes between the two binding assumptions. Although PBPK models ignoring concentration-dependent binding may adequately fit observed data, they can necessitate compensatory adjustments in disposition parameters, limiting their ability to predict clinical scenarios beyond the model's original development settings.
APOE4 genotypes and the trajectory of biomarkers, neuroimaging, and cognitive measures in Alzheimer's Disease: A mixed-effects disease progression model.
Essenburg C, Ramanathan M
J Pharmacokinet Pharmacodyn · 2025 Aug · PMID 40884585 · Publisher ↗
BACKGROUND: The ε4 allele of the apolipoprotein E gene (APOE4) is a major risk factor for developing sporadic Alzheimer's disease (AD). APOE4 homozygosity has been recently proposed as the defining signature of a genetic...
BACKGROUND: The ε4 allele of the apolipoprotein E gene (APOE4) is a major risk factor for developing sporadic Alzheimer's disease (AD). APOE4 homozygosity has been recently proposed as the defining signature of a genetic form of AD. The goal was to assess the role, if any, of APOE4 in AD progression using a mixed-effects disease progression model-informed approach. METHODS: Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were analyzed for 2092 participants categorized as cognitively normal (CN), subjective memory concerns (SMC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), or AD. Each included subject had a median of 5.00 (IQR: 3-8) follow-ups; there were n = 13,699 follow-ups. Demographics, APOE4 genotype, cerebrospinal fluid biomarkers, MRI measures, and neuropsychological tests from baseline to 6-years of follow-up visits were analyzed. Linear mixed-effects models were used to evaluate the impact of the APOE4 genotype on disease progression. RESULTS: APOE4 heterozygous and homozygous frequencies were higher in AD vs. CN (p < 0.001). APOE4-positive groups were associated with lower levels of amyloid β1-42, higher levels of Tau and phosphorylated tau-181 proteins, lower hippocampus and entorhinal volumes, and worse AD Assessment Scale Cognitive-11 (ADAS-COG11), ADAS-COG13, and Mini-Mental State Examination neuropsychological test scores. The progression of the biomarkers over time was not associated with APOE4 positivity. The progression of all MRI measures and neuropsychological test scores was associated with APOE4 positivity. CONCLUSIONS: APOE4 genotypes adversely influence the levels of biomarkers and the progression of neuroimaging and cognitive outcomes in AD.
Sample size determination for cardiodynamic ECG assessment using the Concentration-QTc analysis method.
Xue H, Ferber G, Freebern E … +1 more , Darpo B
J Pharmacokinet Pharmacodyn · 2025 Aug · PMID 40877589 · Publisher ↗
Concentration-QTc (C-QTc) analysis was accepted to serve as an alternative to the by-time point analysis with intersection-union test (IUT) as the primary basis for decisions to classify the arrhythmogenic risk of a drug...
Concentration-QTc (C-QTc) analysis was accepted to serve as an alternative to the by-time point analysis with intersection-union test (IUT) as the primary basis for decisions to classify the arrhythmogenic risk of a drug by ICH E14 Q&As (R3) in December 2015. Since then, this analysis method has been widely applied by industry as it significantly reduces the sample size to achieve the same power as with IUT. There are many model-based power calculation approaches available for C-QTc through simulation in the literature, however, there is still no standard method with a clear formula to determine the sample size for C-QTc analysis to exclude a small effect on the QTc interval. The current model-based simulation approaches are too complicated to prevent them from being widely used, which is not commensurate with the popular status. We have developed a systematic method based on t-tests to determine the sample size for different study designs using the C-QTc analysis method and applied it to many studies. The results of the sample sizes utilizing this method are consistent with simulation studies and validated by real analyses.
Dos and don'ts for concentration - QTc analysis as primary analysis for assay sensitivity assessment.
Huang D
J Pharmacokinet Pharmacodyn · 2025 Aug · PMID 40848199 · Publisher ↗
Concentration-QTc (C-QTc) modeling has been widely used as the primary analysis to demonstrate assay sensitivity using moxifloxacin data in regulatory submissions for QT assessment since publication of a scientific white...
Concentration-QTc (C-QTc) modeling has been widely used as the primary analysis to demonstrate assay sensitivity using moxifloxacin data in regulatory submissions for QT assessment since publication of a scientific white paper on C-QTc (Garnett et al. 2018). In this paper, several scientific issues encountered during statistical reviews of such regulatory submissions are discussed. Four common mistakes (don’ts) are described and four recommendations (dos) are made with regard to study design, statistical modeling, and interpretation of results when C-QTc is applied as primary analysis for assay sensitivity assessment. These four dos and don’ts will provide investigators with insights on how to better design and apply appropriate statistical modeling for C-QTc as the primary analysis for assay sensitivity in regulatory QT assessment.
Pharmacometrics education for all by overcoming language barriers to enhance global collaboration.
Khier S, Chan Kwong AHP, Harnichard M … +1 more , Ait-Oudhia S
J Pharmacokinet Pharmacodyn · 2025 Jul · PMID 40739364 · Publisher ↗
Proficiency in English is essential in scientific disciplines; however, it is unevenly distributed globally, creating barriers for those with limited training. Research in neuroscience supports the benefits of teaching i...
Proficiency in English is essential in scientific disciplines; however, it is unevenly distributed globally, creating barriers for those with limited training. Research in neuroscience supports the benefits of teaching in a student's native language. Consequently, pharmacometrics, a complex and growing field, stands to gain significantly from overcoming language barriers to better train future scientists. One effective strategy is to offer pharmacometrics education in various languages, particularly in regions with low English proficiency, such as French-speaking African countries. Recently, two French-led pharmacometrics training programs were conducted in Africa, demonstrating the positive impact of such initiatives. These programs are adaptable to other countries and languages, and ultimately, they could contribute to global health improvements by making pharmacometrics education more accessible worldwide.
Correction: Cross-species translational modelling of targeted therapeutic oligonucleotides using physiologically based pharmacokinetics.
Derbalah A, Stader F, Liu C … +6 more , Zyla A, Abdulla T, Wu Q, Jamei M, Gardner I, Sepp A
J Pharmacokinet Pharmacodyn · 2025 Jul · PMID 40736713 · Full text
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Lessons learned from QT prolongation risk assessment for antibody-drug conjugates in oncology.
Shah SD, Jewell RC, Ferron-Brady G … +1 more , Visser SAG
J Pharmacokinet Pharmacodyn · 2025 Jul · PMID 40721543 · Publisher ↗
Antibody-drug conjugates (ADCs) are advanced cancer therapeutics that link monoclonal antibodies to cytotoxic drugs, enhancing targeted delivery to tumors. Since the FDA's first ADC approval in 2000, 14 ADCs have receive...
Antibody-drug conjugates (ADCs) are advanced cancer therapeutics that link monoclonal antibodies to cytotoxic drugs, enhancing targeted delivery to tumors. Since the FDA's first ADC approval in 2000, 14 ADCs have received approval to date (March 2025), underscoring their therapeutic value across cancer types. A prolonged QT interval is a known risk factor for the development of torsades de pointes (TdP), a potentially fatal ventricular arrhythmia. Therefore, assessing and mitigating the potential for QT prolongation is a fundamental aspect of drug development, especially for oncology therapeutics where patients may already be at an increased risk of cardiovascular complications or receiving other QT-prolonging drugs. Traditional QT risk assessment, as outlined in the ICH E14 guidance, is challenging in oncology due the safety profile of anticancer drugs, which precludes study in healthy participants, and the ethical complications of placebo-controlled studies in patients with cancer; therefore, dedicated QT studies and/or concentration-corrected QT (QTc) assessments have been used as alternative approaches. This review investigates QT risk assessment for FDA-approved ADCs, examining nonclinical and clinical approaches and summarizing the strategies used in informing each ADC's labeling. Findings suggest that ADCs generally exhibit low proarrhythmic risk, attributed to the low systemic concentration of their payloads, and minimal QT effects have been observed in clinical settings. This analysis advocates a streamlined, fit-for-purpose QT risk assessment strategy in ADC development, reducing reliance on dedicated QT studies and promoting integrated assessments in early-phase trials. This approach can optimize ADC safety evaluation, supporting ongoing innovation and therapeutic application in oncology.
Practical guide to concentration-QTc modeling: a hands-on tutorial.
Parkinson J, Dota C, Rekić D
J Pharmacokinet Pharmacodyn · 2025 Jul · PMID 40715879 · Publisher ↗
Concentration-QTc (C-QTc) analysis is a model-based method widely used to assess the impact of drugs on QT interval duration. C-QTc modelling was enabled to be used after the publication of the International Council for...
Concentration-QTc (C-QTc) analysis is a model-based method widely used to assess the impact of drugs on QT interval duration. C-QTc modelling was enabled to be used after the publication of the International Council for Harmonisation (ICH) E14 Questions and Answers guidance document in 2015, followed by the Scientific White Paper on C-QTc modelling (Garnett et al. J Pharmacokinet Pharmacodyn 45(3):383-397 2018), which included technical details and recommendations on how to perform and report the modelling. This hands-on tutorial aims to provide a practical implementation of the recommended C-QTc modelling methodology, including R code to perform the complete analysis, from data formatting to model predictions. The target audience is scientists who will perform C-QTc analyses. The tutorial uses real data from a previously published QT study by (Johannesen et al.Clin Pharmacol Ther 96(5):549-558 2014), focusing on two active treatments (dofetilide and verapamil) and placebo to illustrate positive and negative QT signals. The methodology implemented in this tutorial follows the recommendations outlined in the White paper. This tutorial includes practical steps for preparing an analysis-ready dataset, conducting exploratory data analysis, fitting the linear mixed effects (LME) model, assessing model performance and estimating the upper limit of the two-sided 90% confidence interval (CI) of baseline and placebo-corrected QTc (ΔΔQTc). Reproducibility of this workflow is ensured through the use of pkgr to manage R packages. The R codes provided as part of this tutorial were successfully used for several projects within the AstraZeneca portfolio and accepted by health authorities as part of QTc submissions.
Quantifying clinical and genetic factors influencing rate and severity of autosomal dominant tubulointerstitial kidney disease progression.
Ramesh SS, Rogge M, Kidd KO … +6 more , Williams AH, Yoon DY, Roignot J, Blakeslee K, Bleyer AJ, Kim S
J Pharmacokinet Pharmacodyn · 2025 Jul · PMID 40707830 · Full text
Autosomal dominant tubulointerstitial kidney disease (ADTKD), caused by mutations in UMOD and MUC1 genes, leads to tubular damage and fibrosis, ultimately resulting in kidney failure (KF). This study investigated clinica...
Autosomal dominant tubulointerstitial kidney disease (ADTKD), caused by mutations in UMOD and MUC1 genes, leads to tubular damage and fibrosis, ultimately resulting in kidney failure (KF). This study investigated clinical and genetic factors influencing the rate and severity of ADTKD progression by developing quantitative models. An estimated glomerular filtration rate (eGFR) of 10 mL/min/1.73 m was used to define KF, corresponding to dialysis initiation. Natural history data from the Wake Forest University School of Medicine study were used to develop the models for UMOD (n = 371) and MUC1 (n = 233) disease types (age ≥ 18 years). Longitudinal change in eGFR and time-to-KF were quantified using nonlinear mixed-effects and parametric time-to-event modeling approaches, respectively, in Monolix (version 2024R1). Sigmoid I functions with steepness parameters varying before and after inflection points best captured eGFR decline. Patients with UMOD and MUC1 disease variants exhibited a similar initial shallow steepness ( 1), but after inflection, each declined rapidly. MUC1 patients progressed faster than UMOD during the post-inflection phase (γ₂ = 10.23 vs. 6.34). eGFR at first clinic visit (eGFR_FCV) and age at first clinic visit (AFCV) significantly affected between-subject variability in eGFR decline. A Weibull hazard function best described the time to KF. In UMOD, males reached Te (the age at which approximately 36.8% of individuals remain free from KF) 4 years earlier than females on average (β_Te_Male = -0.07), indicating faster progression in males. Older AFCV was associated with slower progression to KF (β_Te_AFCV = 0.59 for UMOD and 0.81 for MUC1). These models may help enable quantitative data-driven subgroup analysis in the future, optimizing inclusion/exclusion criteria for ADTKD clinical trials.
A semi-mechanistic population pharmacokinetic-pharmacodynamic model to assess downstream drug-target effects on erythropoiesis.
Rognås SV, Schaedeli Stark F, Marchesi M … +2 more , Silber Baumann HE, Abrantes JA
J Pharmacokinet Pharmacodyn · 2025 Jul · PMID 40707717 · Full text
Erythropoiesis is a complex process that results in the production of erythrocytes from hematopoietic stem cells in the bone marrow. This work aimed to develop a population pharmacokinetic-pharmacodynamic (PKPD) model de...
Erythropoiesis is a complex process that results in the production of erythrocytes from hematopoietic stem cells in the bone marrow. This work aimed to develop a population pharmacokinetic-pharmacodynamic (PKPD) model describing erythropoiesis and hemoglobin synthesis following bitopertin, an inhibitor of glycine transporter 1 (GlyT1), administration. Data from a Phase 1 clinical trial in 67 healthy subjects administered bitopertin (10, 30, or 60 mg) or placebo for 120 days were analyzed. Hematological assessments included erythrocyte and reticulocyte counts, immature reticulocyte fraction, hemoglobin concentration, and mean corpuscular hemoglobin. The proposed semi-mechanistic model, which leverages data and physiological knowledge, was found to adequately simultaneously describe the dose- and time-dependent changes in the biomarkers. The framework was used to illustrate the potential outcome of hypothetical drug-target interactions at distinct stages of erythropoiesis and hemoglobin synthesis, exemplifying its usefulness in a clinical setting.
Identification of oncology pharmacokinetic drivers through in vitro experiments and computational modeling.
Boras B, Greenwald EC, Wang Y … +5 more , Shi M, Pascual B, Cianfrogna JA, Bartlett DW, Spilker ME
J Pharmacokinet Pharmacodyn · 2025 Jul · PMID 40699396 · Publisher ↗
Drug discovery balances many factors as it identifies compounds for clinical testing, including compound efficacy, safety, pharmacokinetic (PK) properties, commercial feasibility, competitive positioning, and organizatio...
Drug discovery balances many factors as it identifies compounds for clinical testing, including compound efficacy, safety, pharmacokinetic (PK) properties, commercial feasibility, competitive positioning, and organizational pressures to move quickly with limited knowledge. When considering target engagement within clinically acceptable dosing constraints, design elements often balance potency requirements against the required extent of target engagement, which subsequently inform the PK design criteria (e.g. absorption and half-life considerations). Hence, an early understanding of the magnitude and duration of target engagement can focus design teams by providing well defined design criteria. To this end, an in vitro target engagement assay has been developed to bin targets and compounds by the type of target engagement profile required for efficacy (cellular anti-proliferation). This in turn directionally informs on the required concentration profile most aligned with the efficacy readout, bucketing results into three primary categories that drive efficacy: high transient concentrations, average concentrations, and threshold concentrations. This manuscript will outline the methodology developed for this early target coverage assessment and provide examples with selected compounds spanning molecularly targeted and cytotoxic oncology small molecules.
Physiologically-based pharmacokinetic model for predicting drug-drug interactions perpetrated by posaconazole in healthy subjects with normal weight and obesity: Concomitant use and washout.
Bruno CD, Elmokadem A, Greenblatt DJ … +1 more , Chow CR
J Pharmacokinet Pharmacodyn · 2025 Jul · PMID 40681739 · Full text
Posaconazole is an effective broad-spectrum triazole antifungal used as prophylaxis or to treat invasive Aspergillus and Candida infections in adults and pediatric patients. Posaconazole is a known strong inhibitor of cy...
Posaconazole is an effective broad-spectrum triazole antifungal used as prophylaxis or to treat invasive Aspergillus and Candida infections in adults and pediatric patients. Posaconazole is a known strong inhibitor of cytochrome P4503A4 (CYP3A4) and substrate of P-glycoprotein (P-gp), which may lead to drug-drug interactions (DDIs) when co-administered with CYP3A4-sensitive substrates and warrants modified dosing of sensitive drugs when administered concomitantly with posaconazole. Given the long elimination half-life of posaconazole (26-35 h), there is the potential for DDIs caused by posaconazole after discontinuing the antifungal. Our clinical studies revealed that the half-life of posaconazole is significantly prolonged in subjects with a body mass index (BMI) ≥ 35 kg/m, which may put this population at an increased risk of DDIs after stopping posaconazole. This manuscript describes the development, verification, and validation of a whole-body, physiologically-based pharmacokinetic (PBPK) model which describes the concomitant use and washout DDIs of posaconazole delayed-release tablet (DRT) with victim drugs ranolazine and lurasidone in healthy volunteers of normal weight and with obesity. The key findings of this model are 1) the half-life of posaconazole is significantly prolonged in patients with BMI ≥ 35 kg/m and 2) the mechanism of inhibition of CYP3A4 by posaconazole appears to be irreversible in vivo. This model may be used moving forward to assess the potential for washout DDIs with CYP3A4-sensitive substrates during concomitant use with, and after discontinuing posaconazole in subjects with normal weight and obesity.
Using Fisher Information Matrix to predict uncertainty in covariate effects and power to detect their relevance in Non-Linear Mixed Effect Models in pharmacometrics.
Fayette L, Brendel K, Mentré F
J Pharmacokinet Pharmacodyn · 2025 Jul · PMID 40659916 · Publisher ↗
This work focuses on design of experiments for Pharmacokinetic (PK) and Pharmacodynamic (PD) studies. Non-Linear Mixed Effects Models (NLMEM) modelling allows the identification and quantification of covariates that expl...
This work focuses on design of experiments for Pharmacokinetic (PK) and Pharmacodynamic (PD) studies. Non-Linear Mixed Effects Models (NLMEM) modelling allows the identification and quantification of covariates that explain inter-individual variability (IIV). The Fisher Information Matrix (FIM), computed by linearization, has already been used to predict uncertainty on covariate parameters and power of test to detect statistical significance. A covariate effect is deemed statistically significant if it is different from 0 according to a Wald comparison test and clinically relevant if the ratio of change it causes in the parameter is relevant according to a test inspired by the two one-sided tests (TOST) as in bioequivalence studies. FIM calculation was extended by computing its expectation on the joint distribution of the covariates, discrete and continuous. Three methods were proposed: using a provided sample of covariate vectors, simulating covariate vectors, based on provided independent distributions or on estimated copulas. Thereafter, CI of ratios, power of tests and number of subjects needed to achieve desired confidence were derived. Methods were implemented in a working version of the R package PFIM6.1. A simulation study was conducted under various scenarios, including different sample sizes, sampling points, and IIV. Overall, uncertainty on covariate effects and power of tests were accurately predicted. The method was applied to a population PK model of the drug cabozantinib including 27 covariate relationships. Despite numerous relationships, limited representation of certain covariates, FIM correctly predicted uncertainty, and is therefore suitable for rapidly computing number of subjects needed to achieve given powers.
FDA's insights: implementing new strategies for evaluating drug-induced QTc prolongation.
Ji Y, Johannesen L, Garnett C
J Pharmacokinet Pharmacodyn · 2025 Jun · PMID 40560402 · Full text
The questions and answers (Q&A) document for ICH E14/S7B provides the following advancements for QTc assessment: concentration-QTc modeling (C-QTc) as the primary analysis, accepting alternative approaches (Q&A 5.1 and 6...
The questions and answers (Q&A) document for ICH E14/S7B provides the following advancements for QTc assessment: concentration-QTc modeling (C-QTc) as the primary analysis, accepting alternative approaches (Q&A 5.1 and 6.1) to thorough QT (TQT) studies, and incorporating an integrated nonclinical risk assessment as supporting evidence. Based on QT study reports reviewed by the FDA between 2016 and 2024, changes to the E14 guideline have resulted in a 34% decrease in the proportion of TQT studies, while the use of C-QTc analysis as the primary analysis has significantly increased. Studies using C-QTc instead of by-time analysis as the primary analysis reduced median sample sizes by 67%, 42%, and 35% for parallel, nested crossover, and crossover studies, respectively. The white paper C-QTc model was used for 60% of drugs that prolonged the QTc interval. From 2020 to 2024, reviews incorporating an integrated nonclinical risk assessment have also increased. The advancements in QTc assessments have streamlined QTc assessment and made clinical trials less resource-intensive. As the advancements continue to evolve the drug safety evaluation is likely to become even more adaptive and enable more precise and targeted QTc assessment.
The dawn of a new era: can machine learning and large language models reshape QSP modeling?
Androulakis IP, Cucurull-Sanchez L, Kondic A … +4 more , Mehta K, Pichardo C, Pryor M, Renardy M
J Pharmacokinet Pharmacodyn · 2025 Jun · PMID 40524056 · Full text
Quantitative Systems Pharmacology (QSP) has emerged as a cornerstone of modern drug development, providing a robust framework to integrate data from preclinical and clinical studies, enhance decision-making, and optimize...
Quantitative Systems Pharmacology (QSP) has emerged as a cornerstone of modern drug development, providing a robust framework to integrate data from preclinical and clinical studies, enhance decision-making, and optimize therapeutic strategies. By modeling biological systems and drug interactions, QSP enables predictions of outcomes, optimization of dosing regimens, and personalized medicine applications. Recent advancements in artificial intelligence (AI) and machine learning (ML) hold the potential to significantly transform QSP by enabling enhanced data extraction, fostering the development of hybrid mechanistic ML models, and supporting the introduction of surrogate models and digital twins. This manuscript explores the transformative role of AI and ML in reshaping QSP modeling workflows. AI/ML tools now enable automated literature mining, the generation of dynamic models from data, and the creation of hybrid frameworks that blend mechanistic insights with data-driven approaches. Large Language Models (LLMs) further revolutionize the field by transitioning AI/ML from merely a tool to becoming an active partner in QSP modeling. By facilitating interdisciplinary collaboration, lowering barriers to entry, and democratizing QSP workflows, LLMs empower researchers without deep coding expertise to engage in complex modeling tasks. Additionally, the integration of Artificial General Intelligence (AGI) holds the potential to autonomously propose, refine, and validate models, further accelerating innovation across multiscale biological processes. Key challenges remain in integrating AI/ML into QSP workflows, particularly in ensuring rigorous validation pipelines, addressing ethical considerations, and establishing robust regulatory frameworks to address the reliability and reproducibility of AI-assisted models. Moreover, the complexity of multiscale biological integration, effective data management, and fostering interdisciplinary collaboration present ongoing hurdles. Despite these challenges, the potential of AI/ML to enhance hybrid model development, improve model interpretability, and democratize QSP modeling offers an exciting opportunity to revolutionize drug development and therapeutic innovation. This work highlights a pathway toward a transformative era for QSP, leveraging advancements in AI and ML to address these challenges and drive innovation in the field.
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