Explore the vast range of titles available.

Therapeutic protein administration in both preclinical and clinical studies can result in the formation of anti‐drug antibodies against the therapeutic protein. Anti‐drug antibody formation may alter the pharmacokinetics of the therapeutic protein, reduce its plasma concentrations, increase exposure variability, and may lead to a loss of efficacy and adverse events. In an effort to quantitatively understand the effect of anti‐drug antibodies on the concentration‐time profile of a therapeutic protein, as well as develop effective strategies to mitigate its impact in the preclinical and clinical development of therapeutic proteins, mathematical models have been developed to characterize the therapeutic protein pharmacokinetics and its modulation by anti‐drug antibodies in vivo. Here, we review several different mechanism‐based modeling frameworks, summarize their approaches to predict immunogenicity effects, and explore the merits and limitations of each model.

Recently, a new mechanism of drug–drug interaction (DDI) was reported between agalsidase, a therapeutic protein, and migalastat, a small molecule, both of which are treatment options of Fabry disease. Migalastat is a pharmacological chaperone that stabilizes the native form of both endogenous and exogenous agalsidase. In Fabry patients co-administrated with agalsidase and migalastat, the increase in active agalsidase exposure is considered a pharmacokinetic effect of agalsidase infusion but a pharmacodynamic effect of migalastat administration, which makes this new DDI mechanism even more interesting. To quantitatively characterize the interaction between agalsidase and migalastat in human, a pharmacometric DDI model was developed using literature reported concentration–time data. The final model includes three components: a 1-compartment linear model component for migalastat; a 2-compartment linear model component for agalsidase; and a DDI component where the agalsidase-migalastat complex is formed via second order association constant kon, dissociated with first order dissociation constant koff, and distributed/eliminated with same rates as agalsidase alone, albeit the complex (i.e., bound agalsidase) has higher enzyme activity compared to free agalsidase. The final model adequately captured several key features of the unique interaction between agalsidase and migalastat, and successfully characterized the kinetics of migalastat as well as the kinetics and activities of agalsidase when both drugs were used alone or in combination following different doses. Most parameters were reasonably estimated with good precision. Because the model includes mechanistic basis of therapeutic protein and small molecule pharmacological chaperone interaction, it can potentially serve as a foundational work for DDIs with similar mechanism.

Given the high prevalence of multidrug-resistant (MDR)-TB in high HIV burden settings, it is important to identify potential drug-drug interactions between MDR-TB treatment and widely used nucleoside reverse transcriptase inhibitors (NRTIs) in HIV-positive children. Population pharmacokinetic models were developed for lamivudine (n = 54) and abacavir (n = 50) in 54 HIV-positive children established on NRTIs; 27 with MDR-TB (combinations of high-dose isoniazid, pyrazinamide, ethambutol, ethionamide, terizidone, fluoroquinolones, and amikacin), and 27 controls without TB. Two-compartment models with first-order elimination and transit compartment absorption described both lamivudine and abacavir pharmacokinetics, respectively. Allometric scaling with body weight adjusted for the effect of body size. Clearance was predicted to reach half its mature value ∼ 2 (lamivudine) and ∼ 3 (abacavir) months after birth, with completion of maturation for both drugs at ∼ 2 years. No significant difference was found in key pharmacokinetic parameters of lamivudine and abacavir when co-administered with routine drugs used for MDR-TB in HIV-positive children.

Combining amoxicillin with the immunostimulatory toll-like receptor 4 agonist monophosphoryl lipid A (MPLA) represents an innovative approach for enhancing antibacterial treatment success. Exploiting pharmacokinetic and pharmacodynamic data from an infection model of Streptococcus pneumoniae infected mice, we aimed to evaluate the preclinical exposure-response relationship of amoxicillin with MPLA coadministration and establish a link to survival. Antibiotic serum concentrations, bacterial numbers in lung and spleen and survival data of mice being untreated or treated with amoxicillin (four dose levels), MPLA, or their combination were analyzed by nonlinear mixed-effects modelling and time-to-event analysis using NONMEM® to characterize these treatment regimens. On top of a pharmacokinetic interaction, regarding the pharmacodynamic effects the combined treatment was superior to both monotherapies: The amoxicillin efficacy at highest dose was increased by a bacterial reduction of 1.74 log10 CFU/lung after 36 h and survival was increased 1.35-fold to 90.3% after 14 days both compared to amoxicillin alone. The developed pharmacometric pharmacokinetic/pharmacodynamic disease-treatment-survival models provided quantitative insights into a novel treatment option against pneumonia revealing a pharmacokinetic interaction and enhanced activity of amoxicillin and the immune system stimulator MPLA in combination. Further development of this drug combination flanked with pharmacometrics towards the clinical setting seems promising.

Plasma drug concentrations have historically played a central role in pharmacology, serving as a measurable intermediary between administered dose and clinical response. This model, linking Dose, Concentration and Effect, underpins therapeutic drug monitoring, pharmacokinetic/pharmacodynamic (PK/PD) modeling, and regulatory evaluation. Yet, numerous examples challenge the assumption that plasma concentrations are necessary or sufficient to predict drug effects. Drugs acting locally, exhibiting delayed pharmacodynamics, or relying on active metabolites often dissociate systemic levels from clinical efficacy. Furthermore, modern tools such as receptor occupancy imaging, functional biomarkers, and systems pharmacology offer richer representations of drug action. Drawing on Judea Pearl’s framework for causal inference, we question whether plasma concentrations lie on the true causal pathway between dose and effect, or whether they sometimes obscure rather than reveal pharmacological mechanisms. Using clinical examples and conceptual analysis, we argue for a more selective targeted and context-sensitive use of plasma concentrations. This approach values their usefulness while cautioning against overuse. A structured decision framework is proposed to help determine when plasma monitoring is informative, and when alternative approaches may be more appropriate.

Incorporating realistic sets of patient-associated covariates, i.e., virtual populations, in pharmacometric simulation workflows is essential to obtain realistic model predictions. Current covariate simulation strategies often omit or simplify dependency structures between covariates. Copula models are multivariate distribution functions suitable to capture dependency structures between covariates with improved performance compared to standard approaches. We aimed to develop and evaluate a copula model for generation of adult virtual populations for 12 patient-associated covariates commonly used in pharmacometric simulations, using the publicly available NHANES database, including sex, race-ethnicity, body weight, albumin, and several biochemical variables related to organ function. A multivariate (vine) copula was constructed from bivariate relationships in a stepwise fashion. Covariate distributions were well captured for the overall and subgroup populations. Based on the developed copula model, a web application was developed. The developed copula model and associated web application can be used to generate realistic adult virtual populations, ultimately to support model-based clinical trial design or dose optimization strategies.

Long-acting injectable medicinal products (LAIs) prolong drug release and thereby aim to enhance adherence and patient outcomes. European regulatory guidelines require the conduct of single- and multiple-dose trials to exclude differences in drug release between non-steady and steady state conditions. The complexity of these trials may however hamper the development of LAIs. This study aimed to examine whether drug release is different after single- and multiple-dose administration using clinical pharmacokinetic (PK) data of a sample of five regulatory-approved LAIs. Single- and multiple-dose data were extracted from an internal regulatory database. Population pharmacokinetic models with different absorption structures were developed using nonlinear mixed-effect modeling based on the single-dose data of every LAI. The best-fitting models were used to predict the pharmacokinetic profiles after multiple-dose administration. The absorption of LAIs after single-dose administration was best described with (parallel) first-order absorption structures (with and without lag-time). After multiple-dose administration, the mean model accuracy was 93% (minimum to maximum: 70%-122%), and 7 out of 10 observed pharmacokinetic variables (i.e., area under the plasma concentration-time curve, minimum and maximum concentration) met the pre-specified acceptance criteria. In conclusion, multiple-dose PK characteristics can be predicted using models developed from single-dose PK data, which indicates that drug release may not be very different between dosing conditions in this sample of regulatory-approved LAIs. Nevertheless, additional studies on other LAIs are required to test the generalizability of our findings and to increase our understanding of the limitations of the proposed model-based approach vis-à-vis the current evidentiary standard.

Background. Recent reports indicated high miltefosine treatment failure rates for visceral leishmaniasis (VL) on the Indian subcontinent. To further explore the pharmacological factors associated with these treatment failures, a population pharmacokinetic-pharmacodynamic study was performed to examine the relationship between miltefosine drug exposure and treatment failure in a cohort of Nepalese patients with VL. Methods. Miltefosine steady-state blood concentrations at the end of treatment were analyzed using liquid chromatography tandem mass spectrometry. A population pharmacokinetic-pharmacodynamic analysis was performed using nonlinear mixed-effects modeling and a logistic regression model. Individual estimates of miltefosine exposure were explored for their relationship with treatment failure. Results. The overall probability of treatment failure was 21%. The time that the blood concentration was > 10 times the half maximal effective concentration of miltefosine (median, 30.2 days) was significantly associated with treatment failure: each 1-day decrease in miltefosine exposure was associated with a 1.08-fold (95% confidence interval, 1.01-1.17) increased odds of treatment failure. Conclusions. Achieving a sufficient exposure to miltefosine is a significant and critical factor for VL treatment success, suggesting an urgent need to evaluate the recently proposed optimal allometric miltefosine dosing regimen. This study establishes the first evidence for a drug exposure-effect relationship for miltefosine in the treatment of VL.

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

To assess ChatGPT 4.0 (ChatGPT) and Gemini Ultra 1.0 (Gemini) large language models on NONMEM coding tasks relevant to pharmacometrics and clinical pharmacology. ChatGPT and Gemini were assessed on tasks mimicking real-world applications of NONMEM. The tasks ranged from providing a curriculum for learning NONMEM, an overview of NONMEM code structure to generating code. Prompts in lay language to elicit NONMEM code for a linear pharmacokinetic (PK) model with oral administration and a more complex model with two parallel first-order absorption mechanisms were investigated. Reproducibility and the impact of “temperature” hyperparameter settings were assessed. The code was reviewed by two NONMEM experts. ChatGPT and Gemini provided NONMEM curriculum structures combining foundational knowledge with advanced concepts (e.g., covariate modeling and Bayesian approaches) and practical skills including NONMEM code structure and syntax. ChatGPT provided an informative summary of the NONMEM control stream structure and outlined the key NONMEM Translator (NM-TRAN) records needed. ChatGPT and Gemini were able to generate code blocks for the NONMEM control stream from the lay language prompts for the two coding tasks. The control streams contained focal structural and syntax errors that required revision before they could be executed without errors and warnings. The code output from ChatGPT and Gemini was not reproducible, and varying the temperature hyperparameter did not reduce the errors and omissions substantively. Large language models may be useful in pharmacometrics for efficiently generating an initial coding template for modeling projects. However, the output can contain errors and omissions that require correction.