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Development of a Quantitative Systems Pharmacology (QSP) model is a long process with many iterative steps. Lack of standard practices for publishing QSP models has resulted in limited model reproducibility within the field. Multiple studies have identified that model reproducibility is a large challenge, especially for QSP models. This work aimed to investigate the causes of QSP model reproducibility issues and suggest standard practices as a potential solution to ensure QSP models are reproducible. In addition, a protocol is suggested as a guidance towards better publication strategy across journals, hoping to enable QSP knowledge preservation., Competing Interests: Declarations Competing interests J.W. and A.Y. are employees of Astellas. A.G. was an employee of Astellas at the time of this work, is presently employed at Merck & Co. Inc. and owns its stocks. As an Associate Editor for JPKPD, A.G. was not involved in the review or decision process for this paper., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Enfortumab vedotin is an antibody-drug conjugate (ADC) comprised of a Nectin-4-directed antibody and monomethyl auristatin E (MMAE), which is primarily eliminated through P-glycoprotein (P-gp)-mediated excretion and cytochrome P450 3A4 (CYP3A4)-mediated metabolism. A physiologically based pharmacokinetic (PBPK) model was developed to predict effects of combined P-gp with CYP3A4 inhibitor/inducer (ketoconazole/rifampin) on MMAE exposure when coadministered with enfortumab vedotin and study enfortumab vedotin with CYP3A4 (midazolam) and P-gp (digoxin) substrate exposure. A PBPK model was built for enfortumab vedotin and unconjugated MMAE using the PBPK simulator ADC module. A similar model was developed with brentuximab vedotin, an ADC with the same valine-citrulline-MMAE linker as enfortumab vedotin, for MMAE drug-drug interaction (DDI) verification using clinical data. The DDI simulation predicted a less-than-2-fold increase in MMAE exposure with enfortumab vedotin plus ketoconazole (MMAE geometric mean ratio [GMR] for maximum concentration [C max ], 1.15; GMR for area under the time-concentration curve from time 0 to last quantifiable concentration [AUC last ], 1.38). Decreased MMAE exposure above 50% but below 80% was observed with enfortumab vedotin plus rifampin (MMAE GMR C max , 0.72; GMR AUC last , 0.47). No effect of enfortumab vedotin on midazolam or digoxin systemic exposure was predicted. Results suggest that combination enfortumab vedotin, P-gp, and a CYP3A4 inhibitor may result in increased MMAE exposure and patients should be monitored for potential adverse effects. Combination P-gp and a CYP3A4 inducer may result in decreased MMAE exposure. No exposure change is expected for CYP3A4 or P-gp substrates when combined with enfortumab vedotin.ClinicalTrials.gov identifier Not applicable., Competing Interests: Declarations Competing interests Mary P. Choules is an employee of Astellas Pharma, Inc. Peiying Zuo was an employee of Astellas at the time of the analysis; any meeting attendance to support this analysis was paid for by Astellas. They report an unpaid role as Secretary and steering committee member of Mathematical and Computational Sciences (MCS) special interest group in International Society of Pharmacometrics (ISoP). Yukio Otsuka is an employee of Astellas Pharma, Inc. and reports stock or stock options with Astellas Pharma, Inc. Amit Garg is an employee of Seagen, Inc. Mei Tang is an employee of Astellas Pharma, Inc. Peter Bonate is an employee of Astellas Pharma, Inc. Research involving human participants and/or animals This PBPK DDI study utilized data from clinical trials conducted in accordance with the Declaration of Helsinki and International Council for Harmonisation guidelines for good clinical practice. Informed consent This PBPK DDI study utilized data from clinical trials in which written/signed informed consent was obtained from patients before screening., (© 2023. The Author(s).)

Clinical studies have found there still exists a lack of gene therapy dose-toxicity and dose-efficacy data that causes gene therapy dose selection to remain elusive. Model informed drug development (MIDD) has become a standard tool implemented throughout the discovery, development, and approval of pharmaceutical therapies, and has the potential to inform dose-toxicity and dose-efficacy relationships to support gene therapy dose selection. Despite this potential, MIDD approaches for gene therapy remain immature and require standardization to be useful for gene therapy clinical programs. With the goal to advance MIDD approaches for gene therapy, in this review we first provide an overview of gene therapy types and how they differ from a bioanalytical, formulation, route of administration, and regulatory standpoint. With this biological and regulatory background, we propose how MIDD can be advanced for AAV-based gene therapies by utilizing physiological based pharmacokinetic modeling and quantitative systems pharmacology to holistically inform AAV and target protein dynamics following dosing. We discuss how this proposed model, allowing for in-depth exploration of AAV pharmacology, could be the key the field needs to treat these unmet disease populations., Competing Interests: Declarations Competing interests All authors are employees of Astellas, which own Astellas Gene Therapy, a gene therapy company., (© 2023. The Author(s).)

Enzalutamide is known to strongly induce cytochrome P450 3A4 (CYP3A4). Furthermore, enzalutamide showed induction and inhibition of P-glycoprotein (P-gp) in in vitro studies. A clinical drug-drug interaction (DDI) study between enzalutamide and digoxin, a typical P-gp substrate, suggested enzalutamide has weak inhibitory effect on P-gp substrates. Direct oral anticoagulants (DOACs), such as apixaban and rivaroxaban, are dual substrates of CYP3A4 and P-gp, and hence it is recommended to avoid co-administration of these DOACs with combined P-gp and strong CYP3A inducers. Enzalutamide's net effect on P-gp and CYP3A for apixaban and rivaroxaban plasma exposures is of interest to physicians who treat patients for venous thromboembolism with prostate cancer. Accordingly, a physiologically-based pharmacokinetic (PBPK) analysis was performed to predict the magnitude of DDI on apixaban and rivaroxaban exposures in the presence of 160 mg once-daily dosing of enzalutamide. The PBPK models of enzalutamide and M2, a major metabolite of enzalutamide which also has potential to induce CYP3A and P-gp and inhibit P-gp, were developed and verified as perpetrators of CYP3A-and P-gp-mediated interaction. Simulation results predicted a 31% decrease in AUC and no change in C max for apixaban and a 45% decrease in AUC and a 25% decrease in C max for rivaroxaban when 160 mg multiple doses of enzalutamide were co-administered. In summary, enzalutamide is considered to decrease apixaban and rivaroxaban exposure through the combined effects of CYP3A induction and net P-gp inhibition. Concurrent use of these drugs warrants careful monitoring for efficacy and safety., (© 2023. The Author(s).)

Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E-R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E-R analysis in oncology clinical drug development are and what metrics of exposure should be considered., (© 2023. The Author(s).)

'Are two populations the same or are they different' is a question that is often faced in clinical pharmacology trials e.g., a pharmacokinetic trial studying a particular drug in racially different groups. To address this question, concentration-time data were simulated from a reference and test population, where in the latter the clearance, sample size, and sampling design were systematically varied. It was of interest to determine whether the estimates of clearance from the two groups were the same or different. Two approaches were used to estimate the empirical Bayes estimates (EBEs) for clearance. One approach developed a population pharmacokinetic model for the reference population and the EBEs for the reference population were estimated from this model. The parameters of the reference population were fixed to their maximum likelihood estimates. The model was then applied to the test population dataset to estimate the EBEs of the test population using the MAXEVAL = 0 option in NONMEM. A second approach, the theta approach, combined the reference and test datasets into a single dataset and used population as a covariate in the model; the EBEs were estimated from this combined model. The power and type I error rate of each approach were calculated for each treatment combination using a variety of statistical tests to determine whether there was a difference in the distribution of the EBEs in the reference population compared to the test population. Our results suggest that either MAXEVAL or theta approaches can be used with informative sampling designs. In addition to reasonable power and type I error, both approaches gave almost identical results under a dense sampling design. To statistically compare the distribution of EBEs of pharmacokinetic parameters from a reference group to that of a test group, a T-test and DTS eCDF test are equally useful., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer 'omics' data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP + ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices., (© 2022. The Author(s).)

ASP8232 is a novel inhibitor of vascular adhesion protein-1 that was under evaluation for reducing residual albuminuria in patients with diabetic kidney disease. To characterize the pharmacokinetics (PK) of ASP8232 and its effect on vascular adhesion protein 1 (VAP-1) plasma activity and VAP-1 concentrations (pharmacodynamics, PD) in an integrated and quantitative manner, a target mediated drug disposition model was developed based on pooled data from four completed clinical trials with ASP8232 in healthy volunteers, and in patients with diabetic kidney disease and diabetic macular edema, respectively. In this model, the binding of ASP8232 to its soluble and membrane-bound target in the central and peripheral compartments were included. The model was able to adequately describe the non-linear PK and PD of ASP8232. The observed difference in PK between healthy volunteers and renally impaired patients could be explained by an effect of baseline estimated glomerular filtration rate on ASP8232 clearance and relative bioavailability. The relationship between ASP8232 concentration and VAP-1 inhibition was successfully established and can be applied to simulate drug exposure and degree of VAP-1 inhibition for any given dose of ASP8232 across the spectrum of renal function.

The vascular adhesion protein-1 (VAP-1) inhibitor ASP8232 reduces albuminuria in patients with type 2 diabetes and chronic kidney disease. A mechanism-based model was developed to quantify the effects of ASP8232 on renal markers from a placebo-controlled Phase 2 study in diabetic kidney disease with 12 weeks of ASP8232 treatment. The model incorporated the available pharmacokinetic, pharmacodynamic (plasma VAP-1 concentration and activity), serum and urine creatinine, serum cystatin C, albumin excretion rate, urinary albumin-to-creatinine ratio, and urine volume information in an integrated manner. Drug-independent time-varying changes and different drug effects could be quantified for these markers using the model. Through simulations, this model provided the opportunity to dissect the relationship and longitudinal association between the estimated glomerular filtration rate and albuminuria and to quantify the pharmacological effects of ASP8232. The developed drug-independent model may be useful as a starting point for other compounds affecting the same biomarkers in a similar time scale.

Clinical trials are often analyzed by examining the means, e.g., what is the mean treatment effect or what is the mean treatment difference, but there are times when analysis of the maximums (or minimums) are of interest. For instance, what is the highest heart rate that could be observed or what the smallest treatment effect that could be expected? While inference on the means is based on the central limit theorem, the corresponding theorem for maximums or minimums is the Fisher-Tippett theorem, also called the extreme value theorem (EVT). This manuscript will introduce EVT to pharmacometricians, particularly block maxima analysis and peak over threshold analysis, and provide examples for how it can be applied to pharmacometric data, particularly the analysis of pharmacokinetics and ECG safety data, like QTcF intervals.

L-praziquantel (PZQ) pharmacokinetic data were analyzed from two relative bioavailability Phase 1 studies in adult, healthy subjects with two new oral dispersion tablet (ODT) formulations of L-PZQ administered under various combinations of co-administration with food, water, and/or crushing. Linear mixed effects models adequately characterized the noncompartmental estimates of the pharmacokinetic profiles in both studies. Dose, food, and formulation were found to significantly affect L-PZQ exposure in both studies. The model for AUC was then extrapolated to children 2-5 years old accounting for enzyme maturation and weight. The predicted exposures were compared to an external Phase 1 study conducted by the Swiss Tropical and Public Health Institute using a currently marketed formulation (Cesol 600 mg immediate-release tablets) and found to be substantially lower than observed. A root cause analysis was completed to identify the reason for failure of the models. Various scenarios were proposed and tested. Two possible reasons for the failure were identified. One reason was that the model did not account for the reduced hepatic clearance seen in patients compared to the healthy volunteer population used to build the model. The second possible reason was that PZQ absorption appears sensitive to meal composition and the model did not account for differences in meals between a standardized Phase 1 unit and clinical sites in Africa. Further studies are needed to confirm our hypotheses.

The International Council for Harmonisation revised the E14 guideline through the questions and answers process to allow concentration-QTc (C-QTc) modeling to be used as the primary analysis for assessing the QTc interval prolongation risk of new drugs. A well-designed and conducted QTc assessment based on C-QTc modeling in early phase 1 studies can be an alternative approach to a thorough QT study for some drugs to reliably exclude clinically relevant QTc effects. This white paper provides recommendations on how to plan and conduct a definitive QTc assessment of a drug using C-QTc modeling in early phase clinical pharmacology and thorough QT studies. Topics included are: important study design features in a phase 1 study; modeling objectives and approach; exploratory plots; the pre-specified linear mixed effects model; general principles for model development and evaluation; and expectations for modeling analysis plans and reports. The recommendations are based on current best modeling practices, scientific literature and personal experiences of the authors. These recommendations are expected to evolve as their implementation during drug development provides additional data and with advances in analytical methodology.

The original version of this article unfortunately contained an error in Equation 1 under the section "Pre-specified linear mixed effects model". The correct equation has given below.

The current method to analyze concentration-QT interval data, which is based on predictions conditional on a best model, fails to take into account the uncertainty of the model. Previous studies have suggested that failure to take into account model uncertainty using a best model approach can result in confidence intervals that are overly optimistic and may be too narrow. Theoretically, more realistic estimates are obtained using model-averaging where the overall point estimate and confidence interval are a weighted-average from a set of candidate models, the weights of which are equal to each model's Akaike weight. Monte Carlo simulation was used to determine the degree of narrowness in the confidence interval for the degree of QT prolongation under a single ascending dose and thorough QT trial design. Results showed that model averaging performed as well as the best model approach under most conditions with no numeric advantage to using a model averaging approach. No difference was observed in the coverage of the confidence intervals when the best model and model averaging was done by AIC, AICc, or BIC, although in certain circumstances the coverage of the confidence interval themselves tended to be too narrow when using BIC. Modelers can continue to use the best model approach for concentration-QT modeling with confidence, although model averaging may offer more face validity, may be of value in cases where there is uncertainty or misspecification in the best model, and be more palatable to a non-technical reviewer than the best model approach.

Galactomannan (GM) is a polysaccharide present in the cell wall of Aspergillus spp. that is released during growth of the organism. It has been successfully used to aide in the diagnosis of invasive aspergillosis allowing for earlier recognition of disease compared to conventional methods. Since its implementation in the clinic as a diagnostic tool, GM has been used in experimental models to measure therapeutic response. Several clinical studies describe the prognostic value of GM. Herein, we review the evidence supporting the utilization of GM antigen as a biomarker to measure response to systemic antifungal therapy.

The purpose of this work was to present a consolidated set of guidelines for the analysis of uncontrolled concomitant medications (ConMed) as a covariate and potential perpetrator in population pharmacokinetic (PopPK) analyses. This white paper is the result of an industry-academia-regulatory collaboration. It is the recommendation of the working group that greater focus be given to the analysis of uncontrolled ConMeds as part of a PopPK analysis of Phase 2/3 data to ensure that the resulting outcome in the PopPK analysis can be viewed as reliable. Other recommendations include: (1) collection of start and stop date and clock time, as well as dose and frequency, in Case Report Forms regarding ConMed administration schedule; (2) prespecification of goals and the methods of analysis, (3) consideration of alternate models, other than the binary covariate model, that might more fully characterize the interaction between perpetrator and victim drug, (4) analysts should consider whether the sample size, not the percent of subjects taking a ConMed, is sufficient to detect a ConMed effect if one is present and to consider the correlation with other covariates when the analysis is conducted, (5) grouping of ConMeds should be based on mechanism (e.g., PGP-inhibitor) and not drug class (e.g., beta-blocker), and (6) when reporting the results in a publication, all details related to the ConMed analysis should be presented allowing the reader to understand the methods and be able to appropriately interpret the results.

The effect of extrapolated area (%AUCextrap) on estimating mean AUCinf in a simulated single-dose clinical trial was examined. Concentration-time (C-t) profiles from 12 to 36 subjects for 1- and 2-compartment models after single dose administration were simulated with increasing right-tailed censoring. Each subject's %AUCextrap and AUCinf was calculated using eight different methods, including noncompartmental analysis (NCA), population-based methods, and maximum likelihood (ML) accounting for censoring. Each method's geometric mean AUCinf and percent relative error (PRE) from the true AUCinf was calculated. This was repeated 100 times and the mean PRE (MPRE) was calculated. Mean %AUCextrap ranged from 1 to ~30 % for the 1-compartment and 2 to 32 % for the 2-compartment model at the lowest and highest degree of censoring, respectively. NCA methods using all subjects to estimate the population mean AUCinf had similar or less bias (within ± 20 %) than when those subjects with >20 % %AUCextrap were removed. Using Cpred compared to Clast in the calculation of individual AUCinf resulted in no performance improvement. Linear mixed effects models to estimate λz and ML methods accounting for censoring resulted in either no improvement or increased bias when censoring was high. Population pharmacokinetic method bias was dependent on the nature of the C-t profile. When the C-t profile declined biphasically, population models had higher bias than NCA methods but were superior when the C-t profile decline in a log-linear manner. It is recommended that subjects with high %AUCextrap should not be removed from the estimation of mean AUCinf in NCA analyses.

The current gap between animal research and clinical development of analgesic drugs presents a challenge for the application of translational PK-PD modeling and simulation. First, animal pain models lack predictive and construct validity to accurately reflect human pain etiologies and, secondly, clinical pain is a multidimensional sensory experience that can't always be captured by objective and robust measures. These challenges complicate the use of translational PK-PD modeling to project PK-PD data generated in preclinical species to a plausible range of clinical doses. To date only a few drug targets identified in animal studies have shown to be successful in the clinic. PK-PD modeling of biomarkers collected during the early phase of clinical development can bridge animal and clinical pain research. For drugs with novel mechanism of actions understanding of the target pharmacology is essential in order to increase the success of clinical development. There is a specific interest in the application of human pain models that can mimic different aspects of acute/chronic pain symptoms and serves as link between animal and clinical pain research. In early clinical development the main objective of PK-PD modeling is to characterize the relationship between target site binding and downstream biomarkers that have a potential link to the clinical endpoint (e.g. readouts from the human pain models) so as to facilitate the selection of doses for proof of concept studies. In patient studies, the role of PK-PD modeling and simulation is to characterize and confirm patient populations in terms of responder profiles with the aim to find the right dose for the right patient.

Monte Carlo simulation was used to assess the performance of linear mixed effect models (LMEMs) to characterize the concentration-effect relationship when both parent and metabolite concentrations were available. Parent and metabolite concentrations were simulated under an experimental design that mimicked a thorough QT study. Simulations were done where both parent and metabolite concentration prolonged double-delta time-matched placebo controlled QTc (ddQTc) intervals. LMEMs were then estimated where parent and metabolite concentrations, parent concentrations alone, or metabolite concentrations alone were used as the independent variables. The relative error in the estimation of the parent and metabolite slope compared to known theoretical values was calculated. Additional simulations were done comparing the estimate of the one-sided upper 95% confidence interval at 1 time unit post-dose in an under- and overfit model compared to the true data generating model. When both parent and metabolite were included in the LMEM and both parent and metabolite prolonged ddQTc intervals, despite some high correlations between parent and metabolite concentrations, under all conditions the parent slope parameter θ(p) was estimated with good accuracy, having a mean relative error within ±20% of the true value, whereas estimation of the metabolite slope θ(m) was overpredicted with bias increasing as the ratio of the metabolite elimination rate constant (K(elm)) to metabolite formation rate constant (K(f)) increased. Variability in θ(m) relative error increased as the metabolite potency decreased. Metabolite ratio had no effect on the ability to estimate the parent or metabolite slope. When only parent or metabolite concentration was used as the independent variable in a LMEM and both parent and metabolite concentrations prolonged ddQTc intervals, severe omitted variable bias resulted. When estimating the one-sided upper 95% confidence interval, underfitted models tended to have higher predicted values which would suggest that an underfitted model would be more likely to declare a "QT effect", whereas an overfitted model resulted in little difference compared to the true model. When time-matched parent and metabolite concentrations are available, both parent and metabolite concentrations should be included in the model simultaneously recognizing that the estimation of the metabolite slope may be biased under certain conditions. It is better to have an overfitted model than an underfitted model, although model simplification through removal of nonsignificant model terms in an overfitted model should not be neglected. Including both parent and metabolite concentrations in a model, even when the metabolite does not affect ddQTc intervals, does not tend to result in biased parameter estimates and in most cases will not result in inflated confidence intervals for predicted values, e.g., around maximal concentrations at the therapeutic dose. It is recommended that when time-matched parent and metabolite concentrations are available, parent and metabolite concentrations not be analyzed separately because either one or the other will result in biased parameter estimates and inflated point estimates and confidence intervals for predicted values.

Vernakalant hydrochloride is a novel, relatively atrial-selective antiarrhythmic agent that rapidly converts atrial fibrillation (AF) to sinus rhythm (SR). This analysis integrates pharmacokinetic (PK) and safety data from 5 clinical trials of patients with AF or atrial flutter (AFL). Patients were initially given a 10-min intravenous (IV) infusion of vernakalant 3 mg/kg or placebo. If SR was not evident after a 15-min observation, then a second 10-min IV infusion of vernakalant 2 mg/kg or placebo was given. Population pharmacokinetic/pharmacodynamic (PK/PD) models were constructed for QT interval prolongation corrected for heart rate by Fridericia's formula (QTcF) and for changes in systolic blood pressure (SBP). The exposure-response relationships for QTcF and SBP were best described by sigmoidal maximum-effect (E (max)) models. For QTcF, the model was characterized by a typical E (max) of 20.3 ms, and by a vernakalant median effective concentration dependent (EC₅₀) on conversion status (4,222 ng/ml for patients converting to SR and 2,276 ng/ml for those remaining in AF/AFL). For SBP, the model was characterized by E (max) of 3.05 mmHg and EC₅₀ of 1,141 ng/ml. Risk of hypotension (SBP <90 mmHg) was primarily associated with low baseline SBP and to a smaller extent with a history of congestive heart failure (CHF); plasma vernakalant concentrations showed a small contribution to the risk of hypotension (relative risk = 1.4 at 4,000 ng/ml), which may be significant with a baseline SBP of <105 mmHg. These results show that vernakalant had a smaller effect on QTcF in patients who demonstrated conversion to SR than those remaining in AF or AFL, and it had a relatively small effect on SBP.