Model-informed drug development (MIDD) is an approach to improve the efficiency of drug development. To promote awareness and application of MIDD in Japan, the Data Science Expert Committee of the Drug Evaluation Committee in the Japan Pharmaceutical Manufacturers Association established a task force, which surveyed MIDD applications for approved products in Japan. This study aimed to reveal the trends and challenges in the use of MIDD by analyzing the survey results. A total of 322 cases approved in Japan between January 2020 and March 2022 as medical products were included in the survey. Modeling analysis was performed in approximately half of the cases (47.8% [154/322]) and formed a major basis for the selection or justification of dosage and administration in approximately one-fourth of the cases [24.2% (78/322)]. Modeling analysis/model-based dose selection was frequently conducted in cases involving monoclonal antibodies, first indication, orphan drugs, and multi-regional trials. Moreover, the survey results indicated that modeling analyses contributed to dose optimization throughout the developmental phases, including changing dose levels from phase II to phase III and dose adjustment in special populations. Japanese data were included in most cases in which modeling analysis was used for dosage selection. Thus, modelling analysis may also address ethnic factors introduced in the ICH E5 and/or E17 guidelines. In summary, this survey is useful for understanding the current status of MIDD use in Japan and for future drug development.

P-glycoprotein (P-gp) is a key efflux transporter and may be involved in drug-drug interactions (DDIs) at the blood-brain barrier (BBB), which could lead to changes in central nervous system (CNS) drug exposure. Morphine is a P-gp substrate and therefore a potential victim drug for P-gp mediated DDIs. It is however unclear if P-gp inhibitors can induce clinically relevant changes in morphine CNS exposure. Here, we used a physiologically-based pharmacokinetic (PBPK) model-based approach to evaluate the potential impact of DDIs on BBB transport of morphine by clinically relevant P-gp inhibitor drugs.The LeiCNS-PK3.0 PBPK model was used to simulate morphine distribution at the brain extracellular fluid (brain) for different clinical intravenous dosing regimens of morphine, alone or in combination with a P-gp inhibitor. We included 34 commonly used P-gp inhibitor drugs, with inhibitory constants and expected clinical P-gp inhibitor concentrations derived from literature. The DDI impact was evaluated by the change in brain exposure for morphine alone or in combination with different inhibitors. Our analysis demonstrated that P-gp inhibitors had a negligible effect on morphine brain exposure in the majority of simulated population, caused by low P-gp inhibition. Sensitivity analyses showed neither major effects of increasing the inhibitory concentration nor changing the inhibitory constant on morphine brain exposure. In conclusion, P-gp mediated DDIs on morphine BBB transport for the evaluated P-gp inhibitors are unlikely to induce meaningful changes in clinically relevant morphine CNS exposure. The developed CNS PBPK modeling approach provides a general approach for evaluating BBB transporter DDIs in humans.

Sepiapterin is an exogenously synthesized new chemical entity that is structurally equivalent to endogenous sepiapterin, a biological precursor of tetrahydrobiopterin (BH), which is a cofactor for phenylalanine hydroxylase. Sepiapterin is being developed for the treatment of hyperphenylalaninemia in pediatric and adult patients with phenylketonuria (PKU). This study employed concentration-QT interval analysis to assess QT prolongation risk following sepiapterin treatment. Data from three phase 1 studies and one phase 3 study were pooled for this analysis. Pediatric and adult PKU patients ≥ 2 years received multiple doses at 60 mg/kg and adult healthy volunteers received a single or multiple doses at 20 or 60 mg/kg. Time-matched triplicate ECG measurements and plasma samples for pharmacokinetic analysis were collected. Prespecified linear mixed models relating ΔQTcF to concentrations of sepiapterin and the major active circulating metabolite BH were developed for the analysis. The analysis demonstrated that there is no QTcF prolongation risk in patients with PKU following sepiapterin dosing at the highest therapeutic dose, 60 mg/kg/day. The final model showed a marginal but negligible QTcF reduction with increasing sepiapterin and BH concentrations. The effect on ΔQTcF was estimated to -2.72 [-3.72, -1.71] and - 1.25 [-2.75, 0.25] ms at mean baseline adjusted BH C of 332 ng/mL (therapeutic exposure) and 675 ng/mL (supratherapeutic exposure) at dose 60 mg/kg, respectively, in PKU patients with food and in healthy volunteers with a high fat diet. Various covariates, such as clinical study ID, age, sex, food effect, race, body weight, and disease status, on QTcF interval were investigated and were found insignificant, except for food effect and age. This study concludes that there is no QTcF prolongation risk in patients with PKU following sepiapterin dosing up to 60 mg/kg/day, and BH and sepiapterin concentrations minimally affect ΔQTcF after adjustment for time, sex, and meal.

Alogliptin is a highly selective inhibitor of dipeptidyl peptidase-4 and primarily excreted as unchanged drug in the urine, and differences in clinical outcomes in renal impairment patients increase the risk of serious adverse reactions. In this study, we developed a comprehensive physiologically-based quantitative systematic pharmacology model of the alogliptin-glucose control system to predict plasma exposure and use glucose as a clinical endpoint to prospectively understand its therapeutic outcomes with varying renal function. Our model incorporates a PBPK model for alogliptin, DPP-4 activity described by receptor occupancy theory, and the crosstalk and feedback loops for GLP-1-GIP-glucagon, insulin, and glucose. Based on the optimization of renal function-dependent parameters, the model was extrapolated to different stages renal impairment patients. Ultimately our model adequately describes the pharmacokinetics of alogliptin, the progression of DPP-4 inhibition over time and the dynamics of the glucose control system components. The extrapolation results endorse the dose adjustment regimen of 12.5 mg once daily for moderate patients and 6.25 mg once daily for severe and ESRD patients, while providing additional reflections and insights. In clinical practice, our model could provide additional information on the in vivo fate of DPP4 inhibitors and key regulators of the glucose control system.

Brain amyloid beta neuritic plaque accumulation is associated with an increased risk of progression to Alzheimer's disease (AD) [Pfeil, J., et al. in Neurobiol Aging 106: 119-129, 2021]. Several studies estimate rates of change in amyloid plaque over time in clinically heterogeneous cohorts with different factors impacting amyloid plaque accumulation from ADNI and AIBL [Laccarino, L., et al. in Annals Clin and Trans Neurol 6: 1113 1120, 2019, Vos, S.J., et al. in Brain 138: 1327-1338, 2015, Lim, Y.Y., et al. in Alzheimer's Dementia 9: 538-545, 2013], but there are no reports using non-linear mixed effect model for amyloid plaque progression over time similar to that existing of disease-modifying biomarkers for other diseases [Cook, S.F. and R.R. Bies in Current Pharmacol Rep 2: 221-230, 2016, Gueorguieva, I., et al. in Alzheimer's Dementia 19: 2253-2264, 2023]. This study describes the natural progression of amyloid accumulation with population mean and between-participant variability for baseline and intrinsic progression rates quantified across the AD spectrum. 1340 ADNI participants were followed over a 10-year period with F-florbetapir PET scans used for amyloid plaque detection. Non-linear mixed effect with stepwise covariate modelling (scm) was used. Change in natural amyloid plaque levels over 10 year period followed an exponential growth model with an intrinsic rate of approx. 3 centiloid units/year. Age, gender, APOE4 genotype and disease stage were important factors on the baseline in the natural amyloid model. In APOE4 homozygous carriers mean baseline amyloid was increased compared to APOE4 non carriers. These results demonstrate natural progression of amyloid plaque in the continuum of AD.

Cancer is one of the most common and fatal diseases worldwide and kills millions of people every year. Cancer drug resistance, lack of efficacy, and safety are significant problems in cancer patients. A multiorgan-on-a-chip (MOC) device consisting of breast and liver compartments was designed with AutoCAD software. The MOC molds were printed by a Formlabs Form 2 3D printer. MDA-MB-231, HepG2, and MCF-10 A cells were used for the MOC experiments. The cell lines were cultured at 37 °C with 5% CO and cell viability was assessed via Alamar blue dye to generate pharmacodynamics (PD) data. Drug concentrations from the cell culture media were analyzed via Agilent 1260 Infinity II HPLC with a Waters Symmetry C18 column and used to generate pharmacokinetics (PK) data. The PK and PD data were modeled and simulated by Monolix and Simulix software, respectively. The safety and efficacy of drug dosing regimens were compared, and the best dosing regimens were selected. This research designed and fabricated a unique MOC consisting of liver and breast compartments that overcomes the need for sealing or assembling. It was used for PK-PD modeling and simulations, and its functionality was proven experimentally. The new MOC will be helpful in preclinical trials to evaluate the efficacy and safety of drugs.

Efgartigimod is a human IgG1 antibody Fc-fragment that lowers IgG levels through blockade of the neonatal Fc receptor (FcRn) and is being evaluated for the treatment of patients with severe autoimmune diseases mediated by pathogenic IgG autoantibodies. Engineered for increased FcRn affinity at both acidic and physiological pH, efgartigimod can outcompete endogenous IgG binding, preventing FcRn-mediated recycling of IgGs and resulting in increased lysosomal degradation. A population pharmacokinetic-pharmacodynamic (PKPD) model including FcRn binding was developed based on data from two healthy volunteer studies after single and repeated administration of efgartigimod. This model was able to simultaneously describe the serum efgartigimod and total IgG profiles across dose groups, using drug-induced FcRn receptor occupancy as driver of total IgG suppression. The model was expanded to describe the PKPD of efgartigimod in cynomolgus monkeys, rabbits, rats and mice. Most species differences were explainable by including the species-specific in vitro affinity for FcRn binding at pH 7.4 and by allometric scaling of the physiological parameters. In vitro-in vivo scaling proved crucial for translation success: the drug effect was over/underpredicted in rabbits/mice when ignoring the lower/higher binding affinity of efgartigimod for these species versus human, respectively. Given the successful model prediction of the PK and total IgG dynamics across species, it was concluded that the PKPD of efgartigimod can be characterized by target binding. From the model, it is suggested that the initial fast decrease of measurable unbound efgartigimod following dosing is the result of combined clearance of free drug and high affinity target binding, while the relatively slow terminal PK phase reflects release of bound drug from the receptor. High affinity target binding protects the drug from elimination and results in a sustained PD effect characterized by an increase in the IgG degradation rate constant with increasing target receptor occupancy.

DZIF-10c (BI 767551) is a recombinant human monoclonal antibody of the IgG1 kappa isotype. It acts as a SARS-CoV-2 neutralizing antibody. DZIF-10c has been developed for both systemic exposure by intravenous infusion as well as for specific exposure to the respiratory tract by application as an inhaled aerosol generated by a nebulizer. An integrated preclinical/clinical semi-mechanistic population pharmacokinetic model was developed to characterize the exposure profile of DZIF-10c in the systemic circulation and lungs. To inform and reduce uncertainty around exposure in the lungs following different methods of dosing, preclinical cynomolgus monkey data was combined with human data using allometric scaling principles. Human serum concentrations of DZIF-10c from two clinical trials were combined with serum/plasma and lung epithelial lining fluid (ELF) concentrations from three preclinical studies to characterize the relationship between dosing, serum/plasma, and lung exposure. The final model was used to predict exposure in the lungs following different routes of administration. Simulations showed that inhalation provides immediate and relevant exposure in the lung ELF at a much lower dose compared with an infusion. Combining inhalation with intravenous therapy results in high and sustained DZIF-10c exposure in the lungs and systemic circulation, thereby combining the benefits of both routes of administration. By combining preclinical data with clinical data (via allometric scaling principles), the developed population pharmacokinetic model reduced uncertainty around exposure in the lungs allowing evaluation of alternative dosing strategies to achieve the desired concentrations of DZIF-10c in human lungs.

Composite scale data consists of numerous categorical questions/items that are often summed as a total score and are commonly utilized as primary endpoints in clinical trials. These endpoints are conceptually discrete and constrained by nature. Item response theory (IRT) is a powerful approach for detecting drug effects in composite scale data from clinical trials, but estimating all parameters requires a large sample size and all item information, which may not be available. Therefore, total score models are often utilized. The most popular total score models are continuous variable (CV) models, but this strategy establishes assumptions that go against the integer nature, and typically also the bounded nature, of data. Bounded integer (BI) and Coarsened grid (CG) models respect the nature of the data. However, their power to detect drug effects has not been as thoroughly studied in clinical trials. When an IRT model is accessible, IRT-informed models (I-BI and I-CV) are promising methods in which the mean and variability of the total score at any position are extracted from the existing IRT model. In this study, total score data were simulated from the MDS-UPDRS motor subscale. Then, the power, type 1 error, and treatment effect bias of six total score models for detecting drug effects in clinical trials were explored. Further, it was investigated how the power, type 1 of error, and treatment effect bias for the I-BI and I-CV models were affected by mis-specified item information from the IRT model. The I-BI model demonstrated the highest statistical power, maintained an acceptable Type I error rate, and exhibited minimal bias, approaching zero. Following that, the I-CV, BI, and CG with Czado transformation (CG_Czado) models provided the maximum power. However, the CG_Czado model had inflated type 1 error under low sample size scenarios in each arm of clinical trials. The CG model among total score models displayed the lowest power and the most inflated type 1 error. Therefore, the results favor the I-BI model when an IRT model is available; otherwise, the BI model.

Current treatment recommendations mainly rely on rule-based protocols defined from evidence-based clinical guidelines, which are difficult to adapt for high-risk patients such as those with renal impairment. Consequently, unsuccessful therapies and the occurrence of adverse drug reactions are common. Within the context of personalized medicine, that tries to deliver the right treatment dose to maximize efficacy and minimize toxicity, the concept of model-informed precision dosing proposes the use of mechanistic models, like physiologically based pharmacokinetic (PBPK) modeling, to predict drug regimes outcomes. Nonetheless, PBPK models have limited capability when computing patients' centric optimized drug doses. Consequently, reinforcement learning (RL) has been previously used to personalize drug dosage. In this work we propose the first PBPK and RL-based precision dosing system for an orally taken drug (benazepril) considering a virtual population of patients with renal disease. Population based PBPK modeling is used in combination with RL for obtaining patient tailored dose regimes. We also perform patient stratification and feature selection to better handle dose tailoring problems. Based on patients' characteristics with best predictive capabilities, benazepril dose regimes are obtained for a population with features' diversity. Obtained regimes are evaluated based on PK parameters considered. Results show that the proof-of-concept approach herein is capable of learning good dosing regimes for most patients. The use of a PopPBPK model allowed to account for intervariability of patient characteristics and be more inclusive considering also non-frequent patients. Impact analysis of patients' features reveals that renal impairment is the main driver affecting RL capabilities.

Antibody drug conjugates (ADC) are a promising class of oncology therapeutics consisting of an antibody conjugated to a payload via a linker. DYP688 is a novel ADC comprising of a signaling protein inhibitor payload (FR900359) that undergoes unique on-antibody inactivation in plasma, resulting in complex pharmacology. To assess the impact of FR inactivation on DYP688 pharmacology and clinical developability, we performed translational modeling of preclinical PK and tumor growth inhibition (TGI) data, accompanied by mechanistic Krogh cylinder tumor modeling. Using a PK-TGI model, we identified a composite exposure-above-tumorostatic concentration (AUC) metric as the PK-driver of efficacy. To underpin the mechanisms behind AUC as the driver of efficacy, we performed quantitative systems pharmacology (QSP) modeling of DYP688 intratumoral pharmacokinetics and pharmacodynamics. Through exploratory simulations, we show that by deviating from canonical ADC design dogma, DYP688 has optimal FR900359 activity despite its transient inactivation. Finally, we performed the successful preclinical to clinical translation of DYP688 PK, including the payload inactivation kinetics, evidenced by good agreement of the predicted PK to the observed interim clinical PK. Overall, this work highlights early quantitative pharmacokinetics as a missing link in the ADC design-developability chasm.

In oncology drug development, overall response rate (ORR) is commonly used as an early endpoint to assess the clinical benefits of new interventions; however, ORR benefit may not always translate into a long-term clinical benefit such as overall survival (OS). Most of the work on developing endpoints based on tumor growth dynamics relies on empirical validation, leading to a lack of generalizability of the endpoints across indications and therapeutic modalities. Additionally, many of these metrics are model-based and do not use data from all the patients. The objective of this work is to use longitudinal tumor size data and new lesion information (that is, the same information used by the ORR) to develop novel endpoints that can improve early clinical decision-making compared to the ORR. We investigate in this work multiple candidate novel endpoints based on tumor size ratio that utilize longitudinal tumor size data from all the patients regardless of their follow-up, rely only on tumor size and new lesion information, and are model-free. An extensive simulation study is conducted, exploring a wide spectrum of tumor size data and overall survival outcomes by modulating a variety of trial characteristics such as slow vs fast tumor growth, high vs low drug efficacy rates, variability in patients' responses, variations in the number of patients, follow-up periods, new lesion rates and survival curve shapes. The proposed novel endpoints based on tumor size ratio consistently outperform the ORR by having a comparable or higher correlation with the OS. Further, the novel endpoints exhibit superior accuracy compared to the ORR in predicting the long-term OS benefit. Retrospective empirical validation on BMS clinical trials confirms our simulation findings. These findings suggest that the tumor size ratio-based endpoints could replace ORR for early clinical decision-making in oncology drug development.

M6495 is a first-in-class NANOBODY molecule and an inhibitor of ADAMTS-5, with the potential to be a disease modifying osteoarthritis drug. In order to investigate the PK/PD (pharmacokinetic and pharmacodynamic) properties of M6495, a single dose study was performed in cynomolgus monkeys with doses up to 6 mg/kg, with the goal of understanding the PK/PD properties of M6495. The neo-epitope ARGS (Alanine-Arginine-Glycine-Serine) generated by cleavage of aggrecan by ADAMTS-5 was used as a target-engagement biomarker. A long-lasting dose-dependent decrease in serum ARGS could be observed after a single dose of M6495 in cynomolgus monkeys. The serum biomarker ARGS decreased to levels below the limit of quantification of the assay in animals which received doses of M6495 of 6 mg/kg and higher, indicating a strong inhibition of ADAMTS-5. Data from the single-dose PK/PD study was combined with data from a multiple dose study, and a non-linear mixed effects model was used to explore the relationship between plasma concentrations of M6495 and the reduction of serum ARGS. The model was subsequently used to inform the clinical phase 1 study design and was successful in predicting the human clinical pharmacokinetics and pharmacodynamics of M6495. In addition to having enabled a Phase 1 trial with M6495, this is the first PK/PD model describing the pharmacodynamics of the neo-epitope ARGS after ADAMTS5 inhibition. It is expected that in the future, this model can be used or adapted to explore the PK/PD relationship between M6495 serum concentrations and the ARGS serum biomarker.

Maribavir is approved for management of post-transplant cytomegalovirus (CMV) infections refractory and/or resistant to CMV therapies at a dose of 400 mg twice daily (BID). Population pharmacokinetic (PopPK) and exposure-response analyses were conducted to support the appropriateness of 400 mg BID dosing. A PopPK model was developed using non-linear mixed-effects modeling with pooled maribavir plasma concentration-time data from phase 1 and 2 studies (from 100 mg up to 1200 mg as single or repeated doses) and the phase 3 SOLSTICE study (400 mg BID). Exposure-response analyses were performed for efficacy, safety, and viral resistance based on data collected in the SOLSTICE study. Maribavir PK after oral administration was adequately described by a two-compartment model with first-order elimination, first-order absorption, and an absorption lag-time. There was no evidence that maribavir PK was affected by age, sex, race, diarrhea, vomiting, disease characteristics, or concomitant use of histamine H blockers, or proton pump inhibitors. In the SOLSTICE study, higher maribavir exposure was not associated with increased probability of achieving CMV DNA viremia clearance, nor with reduced probability of treatment-emergent maribavir-resistant CMV mutations. A statistically significant association with maribavir exposure was identified for taste disturbance, fatigue, and treatment-emergent serious adverse events, while transplant type, enrollment region, CMV DNA level at baseline, and/or CMV resistance at baseline were identified as additional risk factors for these safety outcomes. In conclusion, the findings of these PopPK and exposure-response analyses provide further support for the recommended maribavir dose of 400 mg BID.

In randomized, placebo controlled clinical trials (RCT) in major depressive disorders (MDD), treatment response (TR) is estimated by the change from baseline at study-end (EOS) of the scores of clinical scales used for assessing disease severity. Treatment effect (TE) is estimated by the baseline-adjusted difference at EOS of TR between active treatments and placebo.The TE is function of treatment-specific and, non-specific (NSRT) effect (referred as placebo effect), and placebo response. The conventional statistical approaches used to estimate TE does not account for the potentially confounding effect of NSRT. This pragmatic approach is equivalent to assume that TE is independent of NSRT even if this assumption is not true, leading to potential risks of inflating false negative/positive results in presence of high proportion of subjects with high/low NSRT.The objective of this study was to develop a model informed framework to analyze the outcomes of RCTs using data driven models, non-linear-mixed effect approach, artificial intelligence, and propensity score weighted methodology (PSW) to control the confounding effect of treatment non-specific response on the estimated TE. The secondary objective was to explore the impact of relevant covariates (including the assessment of a dose-response relationship) on the outcomes of pooled data from two RCTs.The proposed PSW approach provides a critical tool for controlling the confounding effect of treatment non-specific response, to increase signal detection and to provide a reliable estimate of the 'true' treatment effect by controlling false negative results associated with excessively high treatment non-specific response.

Pozelimab, a monoclonal antibody directed against C5, is the first and only treatment for adult and pediatric patients (≥ 1 year) with CD55-deficient protein-losing enteropathy (CHAPLE) disease. A target-mediated drug disposition (TMDD) population pharmacokinetic (PopPK) model was developed using pooled data from four phase 1-3 studies to characterize the pharmacokinetics (PK) of total pozelimab and total C5, and to simulate free pozelimab and free C5 to support the dose regimen in patients with CHAPLE disease. A TMDD PopPK model was developed using total pozelimab and total C5 concentration-time data from 106 participants (82 healthy volunteers; 24 patients with paroxysmal nocturnal hemoglobinuria [PNH]). This model was refined and updated to include PK data from 10 patients with CHAPLE disease from a phase 2/3 study. Stochastic simulations predicted concentration-time profiles for total pozelimab, free pozelimab, and free C5, to obtain pozelimab exposure metrics for patients with CHAPLE disease. A two-compartment TMDD model with two binding sites based on the quasi-equilibrium approximation adequately described the concentration-time profiles of total pozelimab and total C5. Body weight was identified as the most important source of pozelimab PK variability; therefore, the dose was adjusted based on body weight for the predominantly pediatric patients with CHAPLE disease. A robust TMDD PopPK model was developed to describe the PK of total pozelimab and total C5 following pozelimab administration. Reliable predictions for individual exposures of total pozelimab and free C5 were possible and supported the 10 mg/kg weight-based dose regimen in patients with CHAPLE disease.

Recently, an optimal dosing algorithm (OptiDose) was developed to compute the optimal drug doses for any pharmacometrics model for a given dosing scenario. In the present work, we enhance the OptiDose concept to compute optimal drug dosing with respect to both efficacy and safety targets. Usually, these are not of equal importance, but one is a top priority, that needs to be satisfied, whereas the other is a secondary target and should be achieved as good as possible without failing the top priority target. Mathematically, this leads to state-constrained optimal control problems. In this paper, we elaborate how to set up such problems and transform them into classical unconstrained optimal control problems which can be solved in NONMEM. Three different optimal dosing tasks illustrate the impact of the proposed enhanced OptiDose method.

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.