Immunogenicity of avelumab in patients with metastatic Merkel cell carcinoma or advanced urothelial carcinoma.

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.

Tumor growth inhibition modeling in patients with second line biliary tract cancer and first line non-small cell lung cancer based on bintrafusp alfa trials.

This analysis aimed to quantify tumor dynamics in patients receiving either bintrafusp alfa (BA) or pembrolizumab, by population pharmacokinetic (PK)-pharmacodynamic modeling, and investigate clinical and molecular covariates describing the variability in tumor dynamics by pharmacometric and machine-learning (ML) approaches. Data originated from two clinical trials in patients with biliary tract cancer (BTC; NCT03833661) receiving BA and non-small cell lung cancer (NSCLC; NCT03631706) receiving BA or pembrolizumab. Individual drug exposure was estimated from previously developed population PK models. Population tumor dynamics models were developed for each drug-indication combination, and covariate evaluations performed using nonlinear mixed-effects modeling (NLME) and ML (elastic net and random forest models) approaches. The three tumor dynamics' model structures all included linear tumor growth components and exponential tumor shrinkage. The final BTC model included the effect of drug exposure (area under the curve) and several covariates (demographics, disease-related, and genetic mutations). Drug exposure was not significant in either of the NSCLC models, which included two, disease-related, covariates in the BA arm, and none in the pembrolizumab arm. The covariates identified by univariable NLME and ML highly overlapped in BTC but showed less agreement in NSCLC analyses. Hyperprogression could be identified by higher tumor growth and lower tumor kill rates and could not be related to BA exposure. Tumor size over time was quantitatively characterized in two tumor types and under two treatments. Factors potentially related to tumor dynamics were assessed using NLME and ML approaches; however, their net impact on tumor size was considered as not clinically relevant.

Semi-mechanistic population pharmacokinetic/pharmacodynamic modeling of a Plasmodium elongation factor 2 inhibitor cabamiquine for prevention and cure of malaria.

Cabamiquine is a novel antimalarial agent that demonstrates the potential for chemoprevention and treatment of malaria. In this article, the dose-exposure-response relationship of cabamiquine was characterized using a population pharmacokinetic (PK)/pharmacodynamic (PD) model, incorporating the effects of cabamiquine on parasite dynamics at the liver and blood stages of malaria infection. Modeling was performed sequentially. First, a three-compartmental population PK model was developed, comprising linear elimination, a transit absorption model in combination with first-order absorption, and a recirculation model. Second, this model was expanded into a PK/PD model using parasitemia data from an induced blood stage malaria (IBSM) human challenge model. To describe the parasite growth and killing in the blood, a turnover model was used. Finally, the liver stage parasite dynamics were characterized using data from a sporozoite challenge model (SpzCh), and system parameters were fixed based on biological plausibility. Cabamiquine concentration in the central compartment was used to drive parasite killing at the blood and liver stages. Blood stage minimum inhibitory concentrations (MICb) were estimated at 7.12 ng/mL [95% confidence interval (CI95%): 6.26-7.88 ng/mL] and 1.28 ng/mL (CI95%: 1.12-1.43 ng/mL) for IBSM and SpzCh populations, respectively, while liver stage MICl was lower (0.61 ng/mL; CI95%: 0.24-0.96 ng/mL). In conclusion, a population PK/PD model was developed by incorporating parasite dynamics and drug activity at the blood and liver stages based on clinical data and biological knowledge. This model can potentially facilitate antimalarial agent development by supporting the efficient selection of the optimal dosing regimen.

Causal chemoprophylactic activity of cabamiquine against Plasmodium falciparum in a controlled human malaria infection: a randomised, double-blind, placebo-controlled study in the Netherlands.

BACKGROUND: Cabamiquine is a novel antimalarial that inhibits Plasmodium falciparum translation elongation factor 2. We investigated the causal chemoprophylactic activity and dose-exposure-response relationship of single oral doses of cabamiquine following the direct venous inoculation (DVI) of P falciparum sporozoites in malaria-naive, healthy volunteers. METHODS: This was a phase 1b, randomised, double-blind, placebo-controlled, adaptive, dose-finding, single-centre study performed in Leiden, Netherlands. Malaria-naive, healthy adults aged 18-45 years were divided into five cohorts and randomly assigned (3:1) to receive cabamiquine or placebo. Randomisation was done by an independent statistician using codes in a permuted block schedule with a block size of four. Participants, investigators, and study personnel were masked to treatment allocation. A single, oral dose regimen of cabamiquine (200, 100, 80, 60, or 30 mg) or matching placebo was administered either at 2 h (early liver-stage) or 96 h (late liver-stage) after DVI. The primary endpoints based on a per-protocol analysis set were the number of participants who developed parasitaemia within 28 days of DVI, time to parasitaemia, number of participants with documented parasite blood-stage growth, clinical symptoms of malaria, and exposure-efficacy modelling. The impact of cabamiquine on liver stages was evaluated indirectly by the appearance of parasitaemia in the blood. The Clopper-Pearson CI (nominal 95%) was used to express the protection rate. The secondary outcomes were safety and tolerability, assessed in those who had received DVI and were administered one dose of the study intervention. The trial was prospectively registered on ClinicalTrials.gov (NCT04250363). FINDINGS: Between Feb 17, 2020 and April 29, 2021, 39 healthy participants were enrolled (early liver-stage: 30 mg [n=3], 60 mg [n=6], 80 mg [n=6], 100 mg [n=3], 200 mg [n=3], pooled placebo [n=6]; late liver-stage: 60 mg [n=3], 100 mg [n=3], 200 mg [n=3], pooled placebo [n=3]). A dose-dependent causal chemoprophylactic effect was observed, with four (67%) of six participants in the 60 mg, five (83%) of six participants in the 80 mg, and all three participants in the 100 and 200 mg cabamiquine dose groups protected from parasitaemia up to study day 28, whereas all participants in the pooled placebo and 30 mg cabamiquine dose group developed parasitaemia. A single, oral dose of 100 mg cabamiquine or higher provided 100% protection against parasitaemia when administered during early or late liver-stage malaria. The median time to parasitaemia in those with early liver-stage malaria was prolonged to 15, 22, and 24 days for the 30, 60, and 80 mg dose of cabamiquine, respectively, compared with 10 days for the pooled placebo. All participants with positive parasitaemia showed documented blood-stage parasite growth, apart from one participant in the pooled placebo group and one participant in the 30 mg cabamiquine group. Most participants did not exhibit any malaria symptoms in both the early and late liver-stage groups, and those reported were mild in severity. A positive dose-exposure-efficacy relationship was established across exposure metrics. The median maximum concentration time was 1-6 h, with a secondary peak observed between 6 h and 12 h in all cabamiquine dose groups (early liver-stage). All cabamiquine doses were safe and well tolerated. Overall, 26 (96%) of 27 participants in the early liver-stage group and ten (83·3%) of 12 participants in the late liver-stage group reported at least one treatment-emergent adverse event (TEAE) with cabamiquine or placebo. Most TEAEs were of mild severity, transient, and resolved without sequelae. The most frequently reported cabamiquine-related TEAE was headache. No dose-related trends were observed in the incidence, severity, or causality of TEAEs. INTERPRETATION: The results from this study show that cabamiquine has a dose-dependent causal chemoprophylactic activity. Together with previously demonstrated activity against the blood stages combined with a half-life of more than 150 h, these results indicate that cabamiquine could be developed as a single-dose monthly regimen for malaria prevention. FUNDING: The healthcare business of Merck KGaA, Darmstadt, Germany.

A multistate modeling and simulation framework to learn dose-response of oncology drugs: Application to bintrafusp alfa in non-small cell lung cancer.

The dose/exposure-efficacy analyses are often conducted separately for oncology end points like best overall response, progression-free survival (PFS) and overall survival (OS). Multistate models offer to bridge these dose-end point relationships by describing transitions and transition times from enrollment to response, progression, and death, and evaluating transition-specific dose effects. This study aims to apply the multistate pharmacometric modeling and simulation framework in a dose optimization setting of bintrafusp alfa, a fusion protein targeting TGF-ß and PD-L1. A multistate model with six states (stable disease [SD], response, progression, unknown, dropout, and death) was developed to describe the totality of endpoints data (time to response, PFS, and OS) of 80 patients with non-small cell lung cancer receiving 500 or 1200 mg of bintrafusp alfa. Besides dose, evaluated predictor of transitions include time, demographics, premedication, disease factors, individual clearance derived from a pharmacokinetic model, and tumor dynamic metrics observed or derived from tumor size model. We found that probabilities of progression and death upon progression decreased over time since enrollment. Patients with metastasis at baseline had a higher probability to progress than patients without metastasis had. Despite dose failed to be statistically significant for any individual transition, the combined effect quantified through a model with dose-specific transition estimates was still informative. Simulations predicted a 69.2% probability of at least 1 month longer, and, 55.6% probability of at least 2-months longer median OS from the 1200 mg compared to the 500 mg dose, supporting the selection of 1200 mg for future studies.

Nontubercular bacterial and fungal infections in patients of chronic obstructive pulmonary disease.

Introduction: Acute exacerbation of chronic obstructive pulmonary disease (COPD), frequently due to respiratory tract infection is the major cause of morbidity and mortality, and estimate suggests that it is currently the third leading cause of death worldwide. Aims and Objectives: This study aims to study the prevalence of nontubercular bacterial and fungal infections in patients of COPD. Materials and Methods: It is an observational study done for 1-year period from August 2017 to July 2018. A total of 100 COPD patients who fulfilled the inclusion and exclusion criteria were analyzed in the present study. These cases were classified according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) combined assessment criteria and subjected to sputum or in some cases Bronchoalveolar lavage (BAL) fluid examination for nontubercular bacterial and fungal pathogens. Serum galactomannan assay, bronchoscopy, and computed tomography chest were done in selected cases. Results: The age of the study population ranged from 40 to 85 years and the mean age was 60.01 ± 9.85 years. Majority of the patients were male (81.0%) and most (78.0%) of them were smokers. Most of the patients belonged to GOLD Grades 2 and 3. Forty-six percent of the patients did show pathogenic organisms in sputum examination. Out of these, 80.4% were bacterial, mainly Gram-negative organisms (Acinetobacter, Pseudomonas, Escherichia coli, Enterobacter, Proteus, and Citrobacter) and 19.6% of cases were having fungal infections (Candida and Aspergillus). Conclusions: Increasing patient age, smoking habit, and severity of COPD were related to an increasing frequency of bacterial and fungal infections. Early detection and proper treatment could help in preventing the morbidity and mortality related to COPD.

Résumé Introduction: L'exacerbation aiguë de la maladie pulmonaire obstructive chronique (MPOC), souvent en raison de l'infection des voies respiratoires, est la principale cause de morbidité et de mortalité, et l'estimation suggère qu'il s'agit actuellement de la troisième cause de décès dans le monde. Objectifs et objectifs: Cette étude vise à étudier la prévalence des infections bactériennes et fongiques non tubulaires chez les patients de la MPOC. Matériaux et méthodes: Il s'agit d'une étude d'observation réalisée pour une période de 1 an d'août 2017 à juillet 2018. Un total de 100 patients atteints de MPOC qui remplissaient les critères d'inclusion et d'exclusion ont été analysés dans la présente étude. Ces cas ont été classés selon l'initiative globale des critères d'évaluation combinés chroniques obstructifs (OR) et soumis à des expectorations ou dans certains cas examen des liquides de lavage bronchoalvéolaire (BAL) pour les agents pathogènes bactéries et fongiques non tubulaires. Le test de galactomannane sérique, la bronchoscopie et le poitrine de tomodensitométrie ont été effectués dans certains cas. Résultats: L'âge de la population d'étude variait de 40 à 85 ans et l'âge moyen était de 60,01 ± 9,85 ans. La majorité des patients étaient des hommes (81,0%) et la plupart (78,0%) d'entre eux étaient des fumeurs. La plupart des patients appartenaient à GOLD GRADES 2 et 3. Quarante-six pour cent des patients ont montré des organismes pathogènes à l'examen des expectorations. Parmi ceux-ci, 80,4% étaient des organismes bactériens, principalement à Gram - négatifs (Acinetobacter, Pseudomonas, Escherichia coli, Enterobacter, Proteus et Citrobacter) et 19,6% des cas avaient des infections fongiques (Candida et 23 aspergillus). Conclusions: L'âge croissant du patient, l'habitude du tabagisme et la gravité de la MPOC étaient liés à une fréquence croissante des infections bactériennes et fongiques. La détection précoce et le traitement approprié pourraient aider à prévenir la morbidité et la mortalité liées à la MPOC. Mots-clés: Maladie pulmonaire obstructive chronique, infection fongique, initiative mondiale pour la maladie pulmonaire obstructive chronique, infection bactérienne non tuberculeuse.

Variable or variate? A conundrum in pharmacometrics exposure-response models.

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.

Model-informed approach for risk management of bleeding toxicities for bintrafusp alfa, a bifunctional fusion protein targeting TGF-ß and PD-L1.

PURPOSE: Bintrafusp alfa (BA) is a bifunctional fusion protein composed of the extracellular domain of the transforming growth factor-ß (TGF-ß) receptor II fused to a human immunoglobulin G1 antibody blocking programmed death ligand 1 (PD-L1). The recommended phase 2 dose (RP2D) was selected based on phase 1 efficacy, safety, and pharmacokinetic (PK)-pharmacodynamic data, assuming continuous inhibition of PD-L1 and TGF-ß is required. Here, we describe a model-informed dose modification approach for risk management of BA-associated bleeding adverse events (AEs). METHODS: The PK and AE data from studies NCT02517398, NCT02699515, NCT03840915, and NCT04246489 (n = 936) were used. Logistic regression analyses were conducted to evaluate potential relationships between bleeding AEs and BA time-averaged concentration (Cavg), derived using a population PK model. The percentage of patients with trough concentrations associated with PD-L1 or TGF-ß inhibition across various dosing regimens was derived. RESULTS: The probability of bleeding AEs increased with increasing Cavg; 50% dose reduction was chosen based on the integration of modeling and clinical considerations. The resulting AE management guidance to investigators regarding temporary or permanent treatment discontinuation was further refined with recommendations on restarting at RP2D or at 50% dose, depending on the grade and type of bleeding (tumoral versus nontumoral) and investigator assessment of risk of additional bleeding. CONCLUSION: A pragmatic model-informed approach for management of bleeding AEs was implemented in ongoing clinical trials of BA. This approach is expected to improve benefit-risk profile; however, its effectiveness will need to be evaluated based on safety data generated after implementation.

COVID-19 infection in patients with haematological disease - A tertiary centre experience from north India.

This retrospective study was aimed to understand the clinical, laboratory, radiological parameters and the outcome of COVID-19 patients with underlying haematological disease. All patients with known haematological disease admitted with COVID-19-positive status from April to August 2020 in the COVID-19 facility of a tertiary care centre in north India, were included. Their medical records were analyzed for outcome and mortality risk factors. Fifty four patients, 37 males, were included in the study. Of these, 36 patients had haematological malignancy and 18 had benign disorder. Fever (95.5%), cough (59.2%) and dyspnoea (31.4%) were the most common symptoms. Nine patients had severe disease at diagnosis, mostly malignant disorders. Overall mortality rate was 37.0 per cent, with high mortality seen in patients with aplastic anaemia (50.0%), acute myeloid (46.7%) and lymphoblastic leukaemia (40.0%). On univariate analysis, Eastern Cooperative Oncology Group performance status >2 [odd ratio (OR) 11.6], COVID-19 severity (OR 8.2), dyspnoea (OR 5.7) and blood product transfusion (OR 6.4) were the predictors of mortality. However, the presence of moderate or severe COVID-19 (OR 16.6, confidence interval 3.8-72.8) was found significant on multivariate analysis. The results showed that patients with haematological malignancies and aplastic anaemia might be at increased risk of getting severe COVID-19 infection and mortality as compared to the general population.

Translation of liver stage activity of M5717, a Plasmodium elongation factor 2 inhibitor: from bench to bedside.

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 IC99 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 IC99 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.

Applying Modeling and Simulations for Rational Dose Selection of Novel Toll-Like Receptor 7/8 Inhibitor Enpatoran for Indications of High Medical Need.

Dual toll-like receptor (TLR) 7 and TLR8 inhibitor enpatoran is under investigation as a treatment for lupus and coronavirus disease 2019 (COVID-19) pneumonia. Population pharmacokinetic/pharmacodynamic (PopPK/PD) model-based simulations, using PK and PD (inhibition of ex vivo-stimulated interleukin-6 (IL-6) and interferon-α (IFN-α) secretion) data from a phase I study of enpatoran in healthy participants, were leveraged to inform dose selection for lupus and repurposed for accelerated development in COVID-19. A two-compartment PK model was linked to sigmoidal maximum effect (Emax ) models with proportional decrease from baseline characterizing the PD responses across the investigated single and multiple doses, up to 200 mg daily for 14 days (n = 72). Concentrations that maintain 50/60/90% inhibition (IC50/60/90 ) of cytokine secretion (IL-6/IFN-α) over 24 hours were estimated and stochastic simulations performed to assess target coverage under different dosing regimens. Simulations suggested investigating 25, 50, and 100 mg enpatoran twice daily (b.i.d.) to explore the anticipated therapeutic dose range for lupus. With 25 mg b.i.d., > 50% of subjects are expected to achieve 60% inhibition of IL-6. With 100 mg b.i.d., most subjects are expected to maintain almost complete target coverage for 24 hours (> 80% subjects IC90,IL-6  = 15.5 ng/mL; > 60% subjects IC90,IFN-α  = 22.1 ng/mL). For COVID-19, 50 and 100 mg enpatoran b.i.d. were recommended; 50 mg b.i.d. provides shorter IFN-α inhibition (median time above IC90  = 13 hours/day), which may be beneficial to avoid interference with the antiviral immune response. Utilization of PopPK/PD models initially developed for lupus enabled informed dose selection for the accelerated development of enpatoran in COVID-19.

Avelumab Dose Selection for Clinical Studies in Pediatric Patients with Solid Tumors.

BACKGROUND AND OBJECTIVE: A phase I/II trial evaluated the safety, antitumor activity, and pharmacokinetics of avelumab (anti-PD-L1 antibody) in pediatric patients with refractory/relapsed solid tumors (NCT03451825). This study aimed to inform avelumab dose selection in pediatric populations using population pharmacokinetic modeling and simulations. METHODS: Patients aged < 18 years with refractory/relapsed solid tumors enrolled in phase I received avelumab 10 or 20 mg/kg intravenously every 2 weeks. A pediatric population pharmacokinetic model was developed via the frequentist prior approach. RESULTS: Pharmacokinetic parameters from 21 patients who received avelumab 10 mg/kg (n = 6) or 20 mg/kg (n = 15) were analyzed. Patients had a wide range of weights and ages (medians, 37.3 kg and 12 years). Exposures with 10-mg/kg dosing were lower vs adult dosing, particularly in patients weighing < 40 kg, whereas 20-mg/kg dosing achieved or exceeded adult exposures, irrespective of body weight. A two-compartment linear model with time-varying clearance using body weight as a covariate, with the frequentist prior approach, best described pediatric data. In this model, optimal overlap in exposure with adult data was achieved with 800 mg every 2 weeks for patients aged ≥ 12 years and weighing ≥ 40 kg, and 15 mg/kg every 2 weeks for patients aged < 12 years or weighing < 40 kg. CONCLUSIONS: Based on exposure matching, the recommended doses for further avelumab studies, including combination studies, are 15 mg/kg every 2 weeks for pediatric patients aged < 12 years or weighing < 40 kg and the adult flat dose of 800 mg every 2 weeks for pediatric patients aged ≥ 12 years and weighing ≥ 40 kg. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT03451825.

Model-informed drug development supporting the approval of the avelumab flat-dose regimen in patients with advanced renal cell carcinoma.

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.

Model-Based Characterization of the Bidirectional Interaction Between Pharmacokinetics and Tumor Growth Dynamics in Patients with Metastatic Merkel Cell Carcinoma Treated with Avelumab.

PURPOSE: Empirical time-varying clearance models have been reported for several immune checkpoint inhibitors, including avelumab (anti-programmed death ligand 1). To investigate the exposure-response relationship for avelumab, we explored semimechanistic pharmacokinetic (PK)-tumor growth dynamics (TGD) models. PATIENTS AND METHODS: Plasma PK data were pooled from three phase I and II trials (JAVELIN Merkel 200, JAVELIN Solid Tumor, and JAVELIN Solid Tumor JPN); tumor size (TS) data were collected from patients with metastatic Merkel cell carcinoma (mMCC) enrolled in JAVELIN Merkel 200. A PK model was developed first, followed by TGD modeling to investigate interactions between avelumab exposure and TGD. A PK-TGD feedback loop was evaluated with simultaneous fitting of the PK and TGD models. RESULTS: In total, 1,835 PK observations and 338 TS observations were collected from 147 patients. In the final PK-TGD model, which included the bidirectional relationship between PK and TGD, avelumab PK was described by a two-compartment model with a positive association between clearance and longitudinal TS, with no additional empirical time-varying clearance identified. TGD was described by first-order tumor growth/shrinkage rates, with the tumor shrinkage rate decreasing exponentially over time; the exponential time-decay constant decreased with increasing drug concentration, representing the treatment effect through tumor shrinkage inhibition. CONCLUSIONS: We developed a TGD model that mechanistically captures the prevention of loss of antitumor immunity (i.e., T-cell suppression in the tumor microenvironment) by avelumab, and a bidirectional interaction between PK and TGD in patients with mMCC treated with avelumab, thus mechanistically describing previously reported time variance of avelumab elimination.

Pharmacometric modeling and machine learning analyses of prognostic and predictive factors in the JAVELIN Gastric 100 phase III trial of avelumab.

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.

Population pharmacokinetic model selection assisted by machine learning.

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.

Safety, pharmacokinetics, and antimalarial activity of the novel plasmodium eukaryotic translation elongation factor 2 inhibitor M5717: a first-in-human, randomised, placebo-controlled, double-blind, single ascending dose study and volunteer infection study.

BACKGROUND: M5717 is the first plasmodium translation elongation factor 2 inhibitor to reach clinical development as an antimalarial. We aimed to characterise the safety, pharmacokinetics, and antimalarial activity of M5717 in healthy volunteers. METHODS: This first-in-human study was a two-part, single-centre clinical trial done in Brisbane, QLD, Australia. Part one was a double-blind, randomised, placebo-controlled, single ascending dose study in which participants were enrolled into one of nine dose cohorts (50, 100, 200, 400, 600, 1000, 1250, 1800, or 2100 mg) and randomly assigned (3:1) to M5717 or placebo. A sentinel dosing strategy was used for each dose cohort whereby two participants (one assigned to M5717 and one assigned to placebo) were initially randomised and dosed. Randomisation schedules were generated electronically by independent, unblinded statisticians. Part two was an open-label, non-randomised volunteer infection study using the Plasmodium falciparum induced blood-stage malaria model in which participants were enrolled into three dose cohorts. Healthy men and women of non-childbearing potential aged 18-55 years were eligible for inclusion; individuals in the volunteer infection study were required to be malaria naive. Safety and tolerability (primary outcome of the single ascending dose study and secondary outcome of the volunteer infection study) were assessed by frequency and severity of adverse events. The pharmacokinetic profile of M5717 was also characterised (primary outcome of the volunteer infection study and secondary outcome of the single ascending dose study). Parasite clearance kinetics (primary outcome of the volunteer infection study) were assessed by the parasite reduction ratio and the corresponding parasite clearance half-life; the incidence of recrudescence up to day 28 was determined (secondary outcome of the volunteer infection study). Recrudescent parasites were tested for genetic mutations (exploratory outcome). The trial is registered with ClinicalTrials.gov (NCT03261401). FINDINGS: Between Aug 28, 2017, and June 14, 2019, 221 individuals were assessed for eligibility, of whom 66 men were enrolled in the single ascending dose study (eight per cohort for 50-1800 mg cohorts, randomised three M5717 to one placebo, and two in the 2100 mg cohort, randomised one M5717 to one placebo) and 22 men were enrolled in the volunteer infection study (six in the 150 mg cohort and eight each in the 400 mg and 800 mg cohorts). No adverse event was serious; all M5717-related adverse events were mild or moderate in severity and transient, with increased frequency observed at doses above 1250 mg. In the single ascending dose study, treatment-related adverse events occurred in three of 17 individuals in the placebo group; no individual in the 50 mg, 100 mg, or 200 mg groups; one of six individuals in each of the 400 mg, 1000 mg, and 1250 mg groups; two of six individuals in the 600 mg group; and in all individuals in the 1800 mg and 2100 mg groups. In the volunteer infection study, M5717-related adverse events occurred in no participants in the 150 mg or 800 mg groups and in one of eight participants in the 400 mg group. Transient oral hypoesthesia (in three participants) and blurred vision (in four participants) were observed in the 1800 mg or 2100 mg groups and constituted an unknown risk; thus, further dosing was suspended after dosing of the two sentinel individuals in the 2100 mg cohort. Maximum blood concentrations occurred 1-7 h after dosing, and a long half-life was observed (146-193 h at doses ≥200 mg). Parasite clearance occurred in all participants and was biphasic, characterised by initial slow clearance lasting 35-55 h (half-life 231·1 h [95% CI 40·9 to not reached] for 150 mg, 60·4 h [38·6 to 138·6] for 400 mg, and 24·7 h [20·4 to 31·3] for 800 mg), followed by rapid clearance (half-life 3·5 h [3·1 to 4·0] for 150 mg, 3·9 h [3·3 to 4·8] for 400 mg, and 5·5 h [4·8 to 6·4] for 800 mg). Recrudescence occurred in three (50%) of six individuals dosed with 150 mg and two (25%) of eight individuals dosed with 400 mg. Genetic mutations associated with resistance were detected in four cases of parasite recrudescence (two individuals dosed with 150 mg and two dosed with 400 mg). INTERPRETATION: The safety, pharmacokinetics, and antimalarial activity of M5717 support its development as a component of a single-dose antimalarial combination therapy or for malaria prophylaxis. FUNDING: Wellcome Trust and the healthcare business of Merck KGaA, Darmstadt, Germany.

Fast screening of covariates in population models empowered by machine learning.

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.

Rabies encephalitis.

Rabies is an almost always fatal disease that physicians and patients dread due to its dismal prognosis and limited treatment options. Transmission of this disease occurs through the bite of dogs and wild animals (like jackal in our case). Other rare forms of transmission may be through inhalation in bat-infested caves and human-to-human transmission by infected corneal transplants, solid organ and tissue transplantation, and sometimes in laboratory settings. Its diagnosis is usually clinical in the absence of availability of special laboratory investigations at the point-of-care facility. Few people have described the role of imaging in diagnosis. We hereby report a patient with rabies encephalitis, having a history of jackal bite and classical MRI findings that we can use for early diagnosis in the absence of typical clinical features and specialised diagnostic testing.