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One of the objectives of Pharmacometry (PMX) population modeling is the identification of significant and clinically relevant relationships between parameters and covariates. Here, we demonstrate how this complex selection task could benefit from supervised learning algorithms using importance scores. We compare various classical methods with three machine learning (ML) methods applied to NONMEM empirical Bayes estimates: random forest, neural networks (NNs), and support vector regression (SVR). The performance of the ML models is assessed using receiver operating characteristic (ROC) curves. The F1 score, which measures test accuracy, is used to compare ML and PMX approaches. Methods are applied to different scenarios of covariate influence based on simulated pharmacokinetics data. ML achieved similar or better F1 scores than stepwise covariate modeling (SCM) and conditional sampling for stepwise approach based on correlation tests (COSSAC). Correlations between covariates and the number of false covariates does not affect the performance of any method, but effect size has an impact. Methods are not equivalent with respect to computational speed; SCM is 30 and 100-times slower than NN and SVR, respectively. The results are validated in an additional scenario involving 100 covariates. Taken together, the results indicate that ML methods can greatly increase the efficiency of population covariate model building in the case of large datasets or complex models that require long run-times. This can provide fast initial covariate screening, which can be followed by more conventional PMX approaches to assess the clinical relevance of selected covariates and build the final model.

A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learning algorithms. We compared the classical pharmacometric approach with two machine learning methods, genetic algorithm and neural networks, in different scenarios based on simulated pharmacokinetic data. Genetic algorithm performance was assessed using a fitness function based on log-likelihood, whilst neural networks were trained using mean square error or binary cross-entropy loss. Machine learning provided a selection based only on statistical rules and achieved accurate selection. The minimization process of genetic algorithm was successful at allowing the algorithm to select plausible models. Neural network classification tasks achieved the most accurate results. Neural network regression tasks were less precise than neural network classification and genetic algorithm methods. The computational gain obtained by using machine learning was substantial, especially in the case of neural networks. We demonstrated that machine learning methods can greatly increase the efficiency of pharmacokinetic population model selection in case of large datasets or complex models requiring long run-times. Our results suggest that machine learning approaches can achieve a first fast selection of models which can be followed by more conventional pharmacometric approaches.

The selection and combination of dose regimens for antimalarials involve complex considerations including pharmacokinetic and pharmacodynamic interactions. In this study, we use immediate ex vivo P. falciparum field isolates to evaluate the effect of cabamiquine and pyronaridine as standalone treatments and in combination therapy. We feed the data into a pharmacometrics model to generate an interaction map and simulate meaningful clinical dose ratios. We demonstrate that the pharmacometrics model of parasite growth and killing provides a detailed description of parasite kinetics against cabamiquine-susceptible and resistant parasites. Pyronaridine monotherapy provides suboptimal killing rates at doses as high as 720 mg. In contrast, the combination of a single dose of 330 mg cabamiquine and 360 mg pyronaridine provides over 90% parasite killing in most of the simulated patients. The described methodology that combines a rapid, 3R-compliant in vitro method and modelling to set meaningful doses for new antimalarials could contribute to clinical drug development. Here the authors use drug susceptibility data from Plasmodium falciparum field isolates in a pharmacometric model to evaluate cabamiquine and pyronaridine efficacy, both individually and in combination. The combined treatment shows over 90% parasite reduction in most simulated cases, providing valuable guidance for clinical dose selection in real-world settings.

Avelumab, a human anti–programmed death ligand 1 immunoglobulin G1 antibody, has shown efficacy and manageable safety in multiple tumors. A two‐compartment population pharmacokinetic model for avelumab incorporating intrinsic and extrinsic covariates and time‐varying clearance (CL) was identified based on data from 1,827 patients across three clinical studies. Of 14 tumor types, a decrease in CL over time was more notable in metastatic Merkel cell carcinoma and squamous cell carcinoma of the head and neck, which had maximum decreases of 32.1% and 24.7%, respectively. The magnitude of reduction in CL was higher in responders than in nonresponders. Significant covariate effects of baseline weight, baseline albumin, and sex were identified on both CL and central distribution volume. Significant covariate effects of black/African American race, C‐reactive protein, and immunogenicity were found on CL. None of the covariate or time‐dependent effects were clinically important or warranted dose adjustment.

Certolizumab pegol (CZP; CIMZIA™) is the only Fc‐free tumor necrosis factor inhibitor with data from a clinical study demonstrating no to minimal placental transfer. The pharmacokinetics (PK) of certolizumab pegol during pregnancy and postpartum in women with chronic inflammatory diseases were assessed using a population PK model based on data from the CHERISH study (NCT04163016), a longitudinal, prospective, open‐label PK phase IB study. Model development was performed in NONMEM using a frequentist prior approach, with prior information based on a population PK model for certolizumab pegol in non‐pregnant adult patients (NCT04740814). A one‐compartment model with first‐order absorption (Ka = 0.236 1/day) and linear elimination (CL/F = 0.416 L/day) from the central compartment (V/F = 7.86 L) best described certolizumab pegol PK in the CHERISH study. The structural model parameters were estimated with good precision (RSE < 25%). Baseline BW was included as a covariate on CL/F and V/F. Pregnancy trimester and time‐varying log‐transformed anti‐drug antibody (ADA) titer were identified as the only significant covariates for CL/F with a comparable influence on CL/F. Individuals with higher ADA titer (75th percentile) during pregnancy exhibited CL/F up to 1.43‐fold higher relative to individuals postpartum that showed median levels of ADA titer. However, the confidence interval for the combined effect of pregnancy stage and ADA titer effects on CL/F overlapped with the CL/F range of the typical individual postpartum. In addition, simulations showed a large overlap in certolizumab pegol concentrations between pregnant and non‐pregnant adults. The findings of this population PK analysis support the maintenance of established certolizumab pegol dosing regimens throughout pregnancy.

Background Targeting the asymptomatic liver stage of Plasmodium infection through chemoprevention could become a key intervention to reduce malaria-associated incidence and mortality. Methods M5717, a Plasmodium  elongation factor 2 inhibitor, was assessed in vitro and in vivo with readily accessible Plasmodium berghei parasites. In an animal refinement, reduction, replacement approach, the in vitro IC 99 value was used to feed a Population Pharmacokinetics modelling and simulation approach to determine meaningful effective doses for a subsequent Plasmodium sporozoite-induced volunteer infection study. Results Doses of 100 and 200 mg would provide exposures exceeding IC 99 in 96 and 100% of the simulated population, respectively. Conclusions This approach has the potential to accelerate the search for new anti-malarials, to reduce the number of healthy volunteers needed in a clinical study and decrease and refine the animal use in the preclinical phase. Graphical Abstract

Key elements of scientific writing—consistency and clarity—can be compromised in case of inaccurate use of methodological terms, especially in complex and multidisciplinary scientific fields. Such is the case in reports of pharmacometrics exposure–response analyses with the use of the terms univariate/multivariate and univariable/multivariable. This perspective outlines the issues in the use of these terms, clarifies their definitions, provides examples, and makes recommendations for authors, reviewers, and journals in the fields of clinical pharmacology and pharmacometrics.

Avelumab (anti–PD‐L1) is an approved anticancer treatment for several indications. The JAVELIN Gastric 100 phase III trial did not meet its primary objective of demonstrating superior overall survival (OS) with avelumab maintenance versus continued chemotherapy in patients with advanced gastric cancer/gastroesophageal junction cancer; however, the OS rate was numerically higher with avelumab at timepoints after 12 months. Machine learning (random forests, SIDEScreen, and variable‐importance assessments) was used to build models to identify prognostic/predictive factors associated with long‐term OS and tumor growth dynamics (TGDs). Baseline, re‐baseline, and longitudinal variables were evaluated as covariates in a parametric time‐to‐event model for OS and Gompertzian population model for TGD. The final OS model incorporated a treatment effect on the log‐logistic shape parameter but did not identify a treatment effect on OS or TGD. Variables identified as prognostic for longer OS included older age; higher gamma‐glutamyl transferase (GGT) or albumin; absence of peritoneal carcinomatosis; lower neutrophil‐lymphocyte ratio, lactate dehydrogenase, or C‐reactive protein (CRP); response to induction chemotherapy; and Eastern Cooperative Oncology Group performance status of 0. Among baseline and time‐varying covariates, the largest effects were found for GGT and CRP, respectively. Liver metastasis at re‐baseline predicted higher tumor growth. Tumor size after induction chemotherapy was associated with number of metastatic sites and stable disease (vs. response). Asian region did not impact OS or TGD. Overall, an innovative workflow supporting pharmacometric modeling of OS and TGD was established. Consistent with the primary trial analysis, no treatment effect was identified. However, potential prognostic factors were identified.

Like other monoclonal antibodies, immune checkpoint inhibitors may be immunogenic in some patients, potentially affecting pharmacokinetics (PKs) and clinical outcomes. In post hoc analyses, we characterized antidrug antibody (ADA) development with avelumab monotherapy in patients with metastatic Merkel cell carcinoma (mMCC) from the JAVELIN Merkel 200 trial (first‐line [1L; N = 116] and second‐line or later [≥2L; N = 88] cohorts) or with advanced urothelial carcinoma (aUC) from the JAVELIN Bladder 100 (1L maintenance [N = 350]) and JAVELIN Solid Tumor (≥2L [N = 249]) trials. Treatment‐emergent ADAs developed in a numerically higher proportion of patients with aUC (1L maintenance, 19.1%; ≥2L, 18.1%) versus mMCC (1L, 8.2%; ≥2L, 8.9%); incidences within tumor types were similar by line of therapy. In PK analyses, numerically lower avelumab trough concentration and higher baseline clearance were observed in treatment‐emergent ADA+ versus ADA− subgroups; however, differences were not clinically relevant. Numerical differences in overall survival, progression‐free survival, or objective response rate by ADA status were observed; however, no clinically meaningful trends were identified. Proportions of patients with treatment‐emergent adverse events (TEAEs; any grade or grade 3/4), serious TEAEs, TEAEs leading to treatment discontinuation, or infusion‐related reactions were similar, with overlapping 80% confidence intervals between ADA subgroups. Efficacy and safety observations were similar in subgroups defined by early development of ADA+ status during treatment. In conclusion, no meaningful differences in PKs, efficacy, and safety were observed between subgroups of avelumab‐treated patients with different ADA status. Overall, these data suggest that ADAs are not relevant for treatment decisions with avelumab.

Avelumab is an anti–PD‐L1 monoclonal antibody approved as monotherapy for Merkel cell carcinoma (MCC) and urothelial carcinoma (UC), and in combination with axitinib for advanced renal cell carcinoma (aRCC). Although initially approved with weight‐based dosing (10 mg/kg intravenously [IV] every 2 weeks [Q2W]), avelumab was subsequently approved for flat dosing (800 mg IV Q2W) based on population pharmacokinetic (PopPK), exposure‐efficacy, and exposure‐safety modeling in MCC and UC. Here, through modeling and simulation, we provide justification for a flat‐dose regimen of avelumab plus axitinib in aRCC. Simulated exposure metrics from the previous monotherapy PopPK model (1827 patients) for both weight‐based and flat‐dose regimens were compared with exposure metrics from treatment‐naive patients with aRCC who received avelumab plus axitinib (488 patients). The aRCC population exposures were derived from a fit‐for‐purpose PopPK model developed using data from monotherapy and combination studies and the existing base structural PopPK model. Exposure‐response relationships for safety were analyzed, including grade ≥3 treatment‐emergent adverse events (TEAEs), any‐grade infusion‐related reactions, and TEAE any‐grade immune‐related adverse events (irAEs). Weight‐based dosing of avelumab in the aRCC population yielded similar PK exposures to the flat‐dose regimen reference exposures in the monotherapy population. Increased avelumab exposure was not associated with increased probabilities of grade ≥3 TEAEs or any‐grade IRRs, although there was a weak association with an increased probability of any‐grade irAEs. Overall, models in aRCC suggest that the avelumab 800‐mg Q2W flat‐dose regimen would provide similar benefits compared with weight‐based dosing with no meaningful change in the probability of AEs.