Review of Dose Justifications for Antibody-Drug Conjugate Approvals from Clinical Pharmacology Perspective: a Focus on Exposure-Response Analyses.

Antibody-drug conjugates (ADCs) are revolutionizing cancer treatment by specific targeting of the cancer cells thereby improving the therapeutic window of the drugs. Nevertheless, they are not free from unwanted toxicities mainly resulting from non-specific targeting and release of the payload. Therefore, the dosing regimen must be optimized through integrated analysis of the risk-benefit profile, to maximize the therapeutic potential. Exposure-response (E-R) analysis is one of the most widely used tools for risk-benefit assessment and it plays a pivotal role in dose optimization of ADCs. However, compared to conventional E-R analysis, ADCs pose unique challenges since they feature properties of both small molecules and antibodies. In this article, we review the E-R analyses that have formed the key basis of dose justification for each of the 12 ADCs approved in the USA. We discuss the multiple analytes and exposure metrics that can be utilized for such analysis and their relevance for safety and efficacy of the treatment. For the endpoints used for the E-R analysis, we were able to uncover commonalities across different ADCs for both safety and efficacy. Additionally, we discuss dose optimization strategies for ADCs which are now a critical component in clinical development of oncology drugs.

Pharmacometrics to Evaluate Dosing of the Patient-Friendly Ivermectin CHILD-IVITAB in Children ≥ 15 kg and <15 kg.

The antiparasitic drug ivermectin is approved for persons > 15 kg in the US and EU. A pharmacometric (PMX) population model with clinical PK data was developed (i) to characterize the effect of the patient-friendly ivermectin formulation CHILD-IVITAB on the absorption process and (ii) to evaluate dosing for studies in children < 15 kg. Simulations were performed to identify dosing with CHILD-IVITAB associated with similar exposure coverage in children ≥ 15 kg and < 15 kg as observed in adults receiving the reference formulation STROMECTOL®. A total of 448 ivermectin concentrations were available from 16 healthy adults. The absorption rate constant was 2.41 h-1 (CV 19%) for CHILD-IVITAB vs. 1.56 h-1 (CV 43%) for STROMECTOL®. Simulations indicated that 250 µg/kg of CHILD-IVITAB is associated with exposure coverage in children < 15 kg consistent with that observed in children ≥ 15 kg and adults receiving 200 µg/kg of STROMECTOL®. Performed analysis confirmed that CHILD-IVITAB is associated with faster and more controlled absorption than STROMECTOL®. Simulations indicate that 250 µg/kg of CHILD-IVITAB achieves equivalent ivermectin exposure coverage in children < 15 kg as seen in children ≥ 15 kg and adults.

Population pharmacokinetic and dose-response models of nepadutant, a selective antagonist of the NK2 receptors, in infants with colic.

AIMS: The aim of the current analyses was to develop a population pharmacokinetic model for nepadutant in infants with colic, and a pharmacokinetic-pharmacodynamic model based on observations of duration of crying and fussing following oral nepadutant administration in infants (3-25 weeks) with colic. METHODS: The models were developed based on data obtained at baseline and following treatment with placebo, nepadutant 0.1 mg/kg or nepadutant 0.5 mg/kg administered for 7 days. A continuous response variable, duration of crying and fussing in minutes within 2 h interval, was assembled based on records from "baby's day" diary. RESULTS: The pharmacokinetics of nepadutant was described by a one-compartment model with first-order absorption and elimination with body weight as a structural covariate incorporated allometrically. For an infant weighing 5.3 kg, the estimated apparent clearance was 68.6 L/h (12% relative standard error) and exhibited large variability (78% coefficient of variation). The pharmacokinetic-pharmacodynamic model described (i) a circadian rhythm in the response with lowest and highest observations at 4 a.m. and 9 p.m., respectively, (ii) a placebo effect increasing and flattening out with time in an exponential manner, and (iii) a statistically significant (P < .01) linearly increasing response with dose. The observed and model predicted relative change in response from baseline was -35% and -28% (95% prediction interval -36%; -19%) following placebo, and -44% and -36% (-46%; -27%) after 0.5 mg/kg. CONCLUSIONS: Population pharmacokinetic and dose-response models were successfully developed characterizing the available nepadutant pharmacokinetics and duration of crying and fussing data in infants.

Clinical pharmacokinetics of antimicrobials in critical care: a narrative review.

INTRODUCTION: The management of critically ill septic patients presents considerable challenges due to multifaceted physiological alterations. Rapid changes such as fluid shifts, hyperdynamic states, and altered renal clearance often require special attention for better clinical outcomes. Vital organ dysfunction, with or without MODS, often necessitates supportive management like RRT, ventilatory support, and ECMO. These interventions can significantly affect the PK/PD of administered antimicrobials, complicating effective treatment. AREA COVERED: Patient-specific parameters such as age, weight, and comorbid illnesses (e.g. cystic fibrosis, burns, and immunocompromised states) are critical determinants of antimicrobial pharmacokinetics. Understanding PK/PD determinants is crucial for developing optimized dosing regimens that enhance therapeutic efficacy and minimize toxicity in critically ill patients. EXPERT OPINION: Incorporating pharmacometrics approaches in dose optimization can significantly improve patient outcomes. This review focuses on the nuances of PK/PD for optimized antimicrobial dosing in critically ill septic patients, emphasizing the importance of individualized treatment plans to address the complex and dynamic needs of this patient population. The adoption of these advanced pharmacokinetic and pharmacodynamic principles into clinical practice is essential for advancing patient care and optimizing therapeutic outcomes in critically ill patients.

Exploring Kp,uu,BBB values smaller than unity in remoxipride: A physiologically-based CNS model approach highlighting brain metabolism in drugs with passive blood-brain barrier transport.

(AIM): Kp,uu,BBB values are crucial indicators of drug distribution into the brain, representing the steady-state relationship between unbound concentrations in plasma and in brain extracellular fluid (brainECF). Kp,uu,BBB values < 1 are often interpreted as indicators of dominant active efflux transport processes at the blood-brain barrier (BBB). However, the potential impact of brain metabolism on this value is typically not addressed. In this study, we investigated the brain distribution of remoxipride, as a paradigm compound for passive BBB transport with yet unexplained brain elimination that was hypothesized to represent brain metabolism. (METHODS): The physiologically-based LeiCNS pharmacokinetic predictor (LeiCNS-PK model) was used to compare brain distribution of remoxipride with and without Michaelis-Menten kinetics at the BBB and/or brain cell organelle levels. To that end, multiple in-house (IV 0.7, 3.5, 4, 5.2, 7, 8, 14 and 16 mg kg-1) and external (IV 4 and 8 mg kg-1) rat microdialysis studies plasma and brainECF data were analysed. (RESULTS): The incorporation of active elimination through presumed brain metabolism of remoxipride in the LeiCNS-PK model significantly improved the prediction accuracy of experimentally observed brainECF profiles of this drug. The model integrated with brain metabolism in both barriers and organelles levels is named LeiCNS-PK3.5. (CONCLUSION): For drugs with Kp,uu,BBB values < 1, not only the current interpretation of dominant BBB efflux transport, but also potential brain metabolism needs to be considered, especially because these may be concentration dependent. This will improve the mechanistic understanding of the processes that determine brain PK profiles.

Application of machine-learning models to predict the ganciclovir and valganciclovir exposure in children using a limited sampling strategy.

Intravenous ganciclovir and oral valganciclovir display significant variability in ganciclovir pharmacokinetics, particularly in children. Therapeutic drug monitoring currently relies on the area under the concentration-time (AUC). Machine-learning (ML) algorithms represent an interesting alternative to Maximum-a-Posteriori Bayesian-estimators for AUC estimation. The goal of our study was to develop and validate an ML-based limited sampling strategy (LSS) approach to determine ganciclovir AUC0-24 after administration of either intravenous ganciclovir or oral valganciclovir in children. Pharmacokinetic parameters from four published population pharmacokinetic models, in addition to the World Health Organization growth curve for children, were used in the mrgsolve R package to simulate 10,800 pharmacokinetic profiles of children. Different ML algorithms were trained to predict AUC0-24 based on different combinations of two or three samples. Performances were evaluated in a simulated test set and in an external data set of real patients. The best estimation performances in the test set were obtained with the Xgboost algorithm using a 2 and 6 hours post dose LSS for oral valganciclovir (relative mean prediction error [rMPE] = 0.4% and relative root mean square error [rRMSE] = 5.7%) and 0 and 2 hours post dose LSS for intravenous ganciclovir (rMPE = 0.9% and rRMSE = 12.4%). In the external data set, the performance based on these two sample LSS was acceptable: rMPE = 0.2% and rRMSE = 16.5% for valganciclovir and rMPE = -9.7% and rRMSE = 17.2% for intravenous ganciclovir. The Xgboost algorithm developed resulted in a clinically relevant individual estimation using only two blood samples. This will improve the implementation of AUC-targeted ganciclovir therapeutic drug monitoring in children.

Interest in antibiotic pharmacokinetic modelling in the context of optimising dosing and reducing resistance: bibliometric analysis.

INTRODUCTION: In the era of problems with resistant bacteria strains, pharmacokinetic (PK) modelling offers ways to optimise antibiotic therapy and minimise the risk of resistance development. This bibliometric study aimed to investigate trends in PK modelling stu-dies. The goal was to provide researchers with comprehensive insight and identify future needs. MATERIAL AND METHODS: We used Bibliometrix, VOSviewer, and CiteSpace to analyse Web of Science articles on antibiotic PK modelling from 1983 to March 2023. RESULTS: We analysed 968 papers following the inclusion criteria and built a keywords co-occurrence map and timeline. The average annual growth rate of subject-related publications was 35.56% between 1983 and 2022, maintaining a continuous upward trend. Roberts J.A., Lipman J., and Wallis S.C. are the three most productive and impactful authors (82, 57, 34 articles, and h-index of 30, 25, 15, respectively). The United States leads in this field of research (29.13% of papers). The most relevant affiliations are the University of Queensland, Royal Brisbane and Women's Hospital, and Monash University. The top three most productive and impactful journals are Antimicrobial Agents and Chemotherapy, Journal of Antimicrobial Chemotherapy, and International Journal of Antimicrobial Agents (181, 83, 47 articles and h-index of 42, 30, 18, respectively). Most articles by keyword clustered on meropenem, vancomycin, and amikacin. Moreover, therapeutic drug monitoring, resistance, antibiotic dosing, target attainment, the intensive care unit, and paediatrics are the most trending aspects. CONCLUSIONS: Given the results of this study, we expect to see a steady increase in interest in exploiting the potential of PK modelling for optimising antibiotic therapy.

Understanding time to peak effect of propofol as sole agent on bispectral index in children aged 2-12 years.

BACKGROUND: The pharmacodynamics of propofol in children have previously been described with the proprietary bispectral index (BIS) as an effect-site marker, and it has been suggested that the rate of onset of propofol might be age dependent, that is, a shorter time to peak effect in younger children. However, these analyses were potentially confounded by co-administered drugs, in particular opioids and benzodiazepines. Thus, the goal of this prospective study was to characterize the influence of age and weight on the onset of hypnotic effects from propofol, reflected by the time to peak of propofol effect-site concentration in the absence of additional drugs. METHODS: A total of 46 healthy children aged 2-12 years presenting for elective surgery were included in our observational cohort study. Solely propofol was administered via a target-controlled infusion pump programmed with the Paedfusor pharmacokinetic model. The BIS and infusion pump data were recorded. The effect of an induction "bolus" was recorded having stopped the pump once a propofol plasma target concentration of 7 µg.mL-1 was achieved. A direct-response and an indirect-response model in the context of nonlinear mixed-effects modeling was used to characterize and compare BIS data in children aged 2-6 years and older children aged 8-12 years. RESULTS: Time to peak of propofol effect-site concentration had a difference (p-value <.01) for age and weight, that is 84 [74, 96] (median [IQR] secs for children aged 2-6 years vs. 99 [91, 113] secs for children aged 8-12 years and 82 [71, 95] secs for weight 11-25 kg vs. 99 [91, 114] secs for weight 30-63 kg). The plasma effect-site equilibration rate constant for propofol had a heterogeneous distribution with a median of 2.36 (IQR: 2.05-2.93; range: 0.83-7.31) per minute but showed a weight-dependent effect in patients with weight below 45 kg. CONCLUSIONS: In children, the age and weight have an influence on time to peak effect of propofol. In the absence of opioids and benzodiazepines, time to peak effect was approximately 20% longer in children aged 8-12 years as compared to younger children. Such clinically relevant age and weight effects are an important consideration in the individualized titration of propofol dosing.

A prediction method for the individual serum concentration and therapeutic effect for optimizing adalimumab therapy in inflammatory bowel disease.

OBJECTIVES: Adalimumab (ADM) therapy is effective for inflammatory bowel disease (IBD), but a significant number of IBD patients lose response to ADM. Thus, it is crucial to devise methods to enhance ADM's effectiveness. This study introduces a strategy to predict individual serum concentrations and therapeutic effects to optimize ADM therapy for IBD during the induction phase. METHODS: We predicted the individual serum concentration and therapeutic effect of ADM during the induction phase based on pharmacokinetic and pharmacodynamic (PK/PD) parameters calculated using the empirical Bayesian method. We then examined whether the predicted therapeutic effect, defined as clinical remission or treatment failure, matched the observed effect. RESULTS: Data were obtained from 11 IBD patients. The therapeutic effect during maintenance therapy was successfully predicted at 40 of 47 time points. Moreover, the predicted effects at each patient's final time point matched the observed effects in 9 of the 11 patients. CONCLUSION: This is the inaugural report predicting the individual serum concentration and therapeutic effect of ADM using the Bayesian method and PK/PD modelling during the induction phase. This strategy may aid in optimizing ADM therapy for IBD.

Mixed effect estimation in deep compartment models: Variational methods outperform first-order approximations.

This work focusses on extending the deep compartment model (DCM) framework to the estimation of mixed-effects. By introducing random effects, model predictions can be personalized based on drug measurements, enabling the testing of different treatment schedules on an individual basis. The performance of classical first-order (FO and FOCE) and machine learning based variational inference (VI) algorithms were compared in a simulation study. In VI, posterior distributions of the random variables are approximated using variational distributions whose parameters can be directly optimized. We found that variational approximations estimated using the path derivative gradient estimator version of VI were highly accurate. Models fit on the simulated data set using the FO and VI objective functions gave similar results, with accurate predictions of both the population parameters and covariate effects. Contrastingly, models fit using FOCE depicted erratic behaviour during optimization, and resulting parameter estimates were inaccurate. Finally, we compared the performance of the methods on two real-world data sets of haemophilia A patients who received standard half-life factor VIII concentrates during prophylactic and perioperative settings. Again, models fit using FO and VI depicted similar results, although some models fit using FO presented divergent results. Again, models fit using FOCE were unstable. In conclusion, we show that mixed-effects estimation using the DCM is feasible. VI performs conditional estimation, which might lead to more accurate results in more complex models compared to the FO method.

Model-Based Interspecies Scaling for Predicting Human Pharmacokinetics of CB 4332, a Complement Factor I Protein.

Interspecies scaling of the pharmacokinetics (PK) of CB 4332, a 150 kDa recombinant complement factor I protein, was performed using traditional and model-based approaches to inform first-in-human dose selection. Plasma concentration versus time data from four preclinical PK studies of single intravenous and subcutaneous (SC) CB 4332 dosing in mice, rats and nonhuman primates (NHPs) were modeled simultaneously using naive pooling including allometric scaling. The human-equivalent dose was calculated using the preclinical no observed adverse effect level (NOAEL) as part of the dose-by-factor approach. Pharmacokinetic modeling of CB 4332 revealed species-specific differences in the elimination, which was accounted for by including an additional rat-specific clearance. Signs of anti-drug antibodies (ADA) formation in all rats and some NHPs were observed. Consequently, an additional ADA-induced clearance parameter was estimated including the time of onset. The traditional dose-by-factor approach calculated a maximum recommended starting SC dose of 0.9 mg/kg once weekly, which was predicted it to result in a trough steady-state concentration lower than the determined efficacy target range for CB 4332 in humans. Model simulations predicted the efficacy target range to be reached using 5 mg/kg once weekly SC dosing.

Synergizing pharmacometrics and pharmacovigilance for medication error management: the case of secukinumab.

PURPOSE: To demonstrate the effective integration of pharmacometrics and pharmacovigilance in managing medication errors, highlighted by a case involving secukinumab in a patient with hidradenitis suppurativa. METHODS: We present the case of a 41-year-old male with progressive hidradenitis suppurativa, unresponsive to multiple antibiotic regimens and infliximab treatment. Due to a medication error, the patient received 300 mg of secukinumab daily for 4 days instead of weekly, totaling 1200 mg. The regional pharmacovigilance center assessed potential toxicity, and a pharmacometric analysis using a population pharmacokinetic model was performed to inform dosing adjustments. RESULTS: Clinical data indicated that the received doses were within a non-toxic range. No adverse effects were observed. Pharmacometric simulations revealed a risk of underexposure due to the dosing error. Based on these simulations, it was recommended to restart monthly secukinumab injections on day 35 after the initial dose. Measured plasma concentrations before re-administration confirmed the model's accuracy. CONCLUSION: This case highlights the crucial collaboration between clinical services, pharmacovigilance, and pharmacometrics in managing medication errors. Such interdisciplinary efforts ensure therapeutic efficacy and patient safety by maintaining appropriate drug exposure levels.

Population pharmacokinetics of TLD-1, a novel liposomal doxorubicin, in a phase I trial.

STUDY OBJECTIVES: TLD-1 is a novel pegylated liposomal doxorubicin (PLD) formulation aiming to optimise the PLD efficacy-toxicity ratio. We aimed to characterise TLD-1's population pharmacokinetics using non-compartmental analysis and nonlinear mixed-effects modelling. METHODS: The PK of TLD-1 was analysed by performing a non-compartmental analysis of longitudinal doxorubicin plasma concentration measurements obtained from a clinical trial in 30 patients with advanced solid tumours across a 4.5-fold dose range. Furthermore, a joint parent-metabolite PK model of doxorubicinentrapped, doxorubicinfree, and metabolite doxorubicinol was developed. Interindividual and interoccasion variability around the typical PK parameters and potential covariates to explain parts of this variability were explored. RESULTS: Medians  ± standard deviations of dose-normalised doxorubicinentrapped+free Cmax and AUC0-∞ were 0.342 ± 0.134 mg/L and 40.1 ± 18.9 mg·h/L, respectively. The median half-life (95 h) was 23.5 h longer than the half-life of currently marketed PLD. The novel joint parent-metabolite model comprised a one-compartment model with linear release (doxorubicinentrapped), a two-compartment model with linear elimination (doxorubicinfree), and a one-compartment model with linear elimination for doxorubicinol. Body surface area on the volumes of distribution for free doxorubicin was the only significant covariate. CONCLUSION: The population PK of TLD-1, including its release and main metabolite, were successfully characterised using non-compartmental and compartmental analyses. Based on its long half-life, TLD-1 presents a promising candidate for further clinical development. The PK characteristics form the basis to investigate TLD-1 exposure-response (i.e., clinical efficacy) and exposure-toxicity relationships in the future. Once such relationships have been established, the developed population PK model can be further used in model-informed precision dosing strategies. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov-NCT03387917-January 2, 2018.

Predicting overall survival from tumor dynamics metrics using parametric statistical and machine learning models: application to patients with RET-altered solid tumors.

In oncology drug development, tumor dynamics modeling is widely applied to predict patients' overall survival (OS) via parametric models. However, the current modeling paradigm, which assumes a disease-specific link between tumor dynamics and survival, has its limitations. This is particularly evident in drug development scenarios where the clinical trial under consideration contains patients with tumor types for which there is little to no prior institutional data. In this work, we propose the use of a pan-indication solid tumor machine learning (ML) approach whereby all three tumor metrics (tumor shrinkage rate, tumor regrowth rate and time to tumor growth) are simultaneously used to predict patients' OS in a tumor type independent manner. We demonstrate the utility of this approach in a clinical trial of cancer patients treated with the tyrosine kinase inhibitor, pralsetinib. We compared the parametric and ML models and the results showed that the proposed ML approach is able to adequately predict patient OS across RET-altered solid tumors, including non-small cell lung cancer, medullary thyroid cancer as well as other solid tumors. While the findings of this study are promising, further research is needed for evaluating the generalizability of the ML model to other solid tumor types.

Determining tissue distribution of the oral antileishmanial agent miltefosine: a physiologically-based pharmacokinetic modeling approach.

Miltefosine (MTS) is the only approved oral drug for treating leishmaniasis caused by intracellular Leishmania parasites that localize in macrophages of the liver, spleen, skin, bone marrow, and lymph nodes. MTS is extensively distributed in tissues and has prolonged elimination half-lives due to its high plasma protein binding, slow metabolic clearance, and minimal urinary excretion. Thus, understanding and predicting the tissue distribution of MTS help assess therapeutic and toxicologic outcomes of MTS, especially in special populations, e.g., pediatrics. In this study, a whole-body physiologically-based pharmacokinetic (PBPK) model of MTS was built on mice and extrapolated to rats and humans. MTS plasma and tissue concentration data obtained by intravenous and oral administration to mice were fitted simultaneously to estimate model parameters. The resulting high tissue-to-plasma partition coefficient values corroborate extensive distribution in all major organs except the bone marrow. Sensitivity analysis suggests that plasma exposure is most susceptible to changes in fraction unbound in plasma. The murine oral-PBPK model was further validated by assessing overlay of simulations with plasma and tissue profiles obtained from an independent study. Subsequently, the murine PBPK model was extrapolated to rats and humans based on species-specific physiological and drug-related parameters, as well as allometrically scaled parameters. Fold errors for pharmacokinetic parameters were within acceptable range in both extrapolated models, except for a slight underprediction in the human plasma exposure. These animal and human PBPK models are expected to provide reliable estimates of MTS tissue distribution and assist dose regimen optimization in special populations.

Simulating Healthy Participant Pharmacokinetics for Renal and Hepatic Impairment Studies: Retrospective Assessment of the Approach.

The recruitment of a parallel, healthy participants (HPs) arm in renal and hepatic impairment (RI and HI) studies is a common strategy to assess differences in pharmacokinetics. Limitations in this approach include the underpowered estimate of exposure differences and the use of the drug in a population for which there is no benefit. Recently, a method was published by Purohit et. al. (2023) that leveraged prior population pharmacokinetic (PopPK) modeling-based simulation to infer the distribution of exposure ratios between the RI/HI arms and HPs. The approach was successful, but it was a single example with a robust model having several iterations of development and fitting to extensive HP data. To test in more studies and models at different stages of development, our catalogue of RI/HI studies was searched, and those with suitable properties and from programs with available models were analyzed with the simulation approach. There were 9 studies included in the analysis. Most studies were associated with models that would have been available at the time (ATT) of the study, and all had a current, final model. For 3 studies, the HP PK was not predicted well by the ATT (2) or final (1) models. In comparison to conventional analysis of variance (ANOVA), the simulation approach provided similar point estimates and confidence intervals of exposure ratios. This PopPK based approach can be considered as a method of choice in situations where the simulation of HP data would not be an extrapolation, and when no other complicating factors are present.

Application of machine learning techniques in population pharmacokinetics/pharmacodynamics modeling.

Population pharmacokinetics/pharmacodynamics (pop-PK/PD) consolidates pharmacokinetic and pharmacodynamic data from many subjects to understand inter- and intra-individual variability due to patient backgrounds, including disease state and genetics. The typical workflow in pop-PK/PD analysis involves the determination of the structure model, selection of the error model, analysis based on the base model, covariate modeling, and validation of the final model. Machine learning is gaining considerable attention in the medical and various fields because, in contrast to traditional modeling, which often assumes linear or predefined relationships, machine learning modeling learns directly from data and accommodates complex patterns. Machine learning has demonstrated excellent capabilities for prescreening covariates and developing predictive models. This review introduces various applications of machine learning techniques in pop-PK/PD research.

ChatGPT and Gemini large language models for pharmacometrics with NONMEM: comment.

This is a correspondence on "Evaluation of ChatGPT and Gemini large language models for pharmacometrics with NONMEM". Additional concern on using ChatGPT and Gemini is provided.