WhatsApp-based intervention for people with type 2 diabetes: A randomized controlled trial

Diabetes mellitus is a metabolic disease characterized by prolonged elevated blood glucose levels. Diabetes self-management education and support programs are widely used in western countries. The impact of social media education and support interventions such as a WhatsApp-based program and the nurses' role in supporting and implementing this self-management program unclear. Using a WhatsApp-based program, we evaluated the effects of a 6-week program in improving self-efficacy and education among people with type 2 diabetes mellitus in Saudi Arabia. Eligible participants (n = 80) were recruited with the support of nurses into a randomized controlled trial and randomly assigned into self-management intervention and control groups. The intervention group (n = 40) received the self-management program support and the usual care. The control group (n = 40) received only the usual care with nurses' support. Results from generalized estimating equation analysis showed a significant increase in self-efficacy, self-management, and education in the WhatsApp-based intervention support group compared with the control group at 6 and 12 weeks (follow-up). Implementing the program via social media improves self-efficacy. The use of social media platforms should be promoted for global diabetes management.

Warfarin-A natural anticoagulant: A review of research trends for precision medication

BACKGROUND

Warfarin is a widely prescribed anticoagulant in the clinic. It has a more considerable individual variability, and many factors affect its variability. Mathematical models can quantify the quantitative impact of these factors on individual variability.

PURPOSE

The aim is to comprehensively analyze the advanced warfarin dosing algorithm based on pharmacometrics and machine learning models of personalized warfarin dosage.

METHODS

A bibliometric analysis of the literature retrieved from PubMed and Scopus was performed using VOSviewer. The relevant literature that reported the precise dosage of warfarin calculation was retrieved from the database. The multiple linear regression (MLR) algorithm was excluded because a recent systematic review that mainly reviewed this algorithm has been reported. The following terms of quantitative systems pharmacology, mechanistic model, physiologically based pharmacokinetic model, artificial intelligence, machine learning, pharmacokinetic, pharmacodynamic, pharmacokinetics, pharmacodynamics, and warfarin were added as MeSH Terms or appearing in Title/Abstract into query box of PubMed, then humans and English as filter were added to retrieve the literature.

RESULTS

Bibliometric analysis revealed important co-occuring MeShH and index keywords. Further, the United States, China, and the United Kingdom were among the top countries contributing in this domain. Some studies have established personalized warfarin dosage models using pharmacometrics and machine learning-based algorithms. There were 54 related studies, including 14 pharmacometric models, 31 artificial intelligence models, and 9 model evaluations. Each model has its advantages and disadvantages. The pharmacometric model contains biological or pharmacological mechanisms in structure. The process of pharmacometric model development is very time- and labor-intensive. Machine learning is a purely data-driven approach; its parameters are more mathematical and have less biological interpretation. However, it is faster, more efficient, and less time-consuming. Most published models of machine learning algorithms were established based on cross-sectional data sourced from the database.

CONCLUSION

Future research on personalized warfarin medication should focus on combining the advantages of machine learning and pharmacometrics algorithms to establish a more robust warfarin dosage algorithm. Randomized controlled trials should be performed to evaluate the established algorithm of warfarin dosage. Moreover, a more user-friendly and accessible warfarin precision medicine platform should be developed.

Characterization of Pediatric Rectal Absorption, Drug Disposition, and Sedation Level for Midazolam Gel Using Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling

This study aims to explore and characterize the role of pediatric sedation via rectal route. A pediatric physiologically based pharmacokinetic-pharmacodynamic (PBPK/PD) model of midazolam gel was built and validated to support dose selection for pediatric clinical trials. Before developing the rectal PBPK model, an intravenous PBPK model was developed to determine drug disposition, specifically by describing the ontogeny model of the metabolic enzyme. Pediatric rectal absorption was developed based on the rectal PBPK model of adults. The improved Weibull function with permeability, surface area, and fluid volume parameters was used to extrapolate pediatric rectal absorption. A logistic regression model was used to characterize the relationship between the free concentrations of midazolam and the probability of sedation. All models successfully described the PK profiles with absolute average fold error (AAFE) < 2, especially our intravenous PBPK model that extended the predicted age to preterm. The simulation results of the PD model showed that when the free concentrations of midazolam ranged from 3.9 to 18.4 ng/mL, the probability of "Sedation" was greater than that of "Not-sedation" states. Combined with the rectal PBPK model, the recommended sedation doses were in the ranges of 0.44-2.08 mg/kg for children aged 2-3 years, 0.35-1.65 mg/kg for children aged 4-7 years, 0.24-1.27 mg/kg for children aged 8-12 years, and 0.20-1.10 mg/kg for adolescents aged 13-18 years. Overall, this model mechanistically quantified drug disposition and effect of midazolam gel in the pediatric population, accurately predicted the observed clinical data, and simulated the drug exposure for sedation that will inform dose selection for following pediatric clinical trials.

Physiologically Based Biopharmaceutics Modeling (PBBM): Best Practices for Drug Product Quality, Regulatory and Industry Perspectives: 2023 Workshop Summary Report

Physiologically based biopharmaceutics modeling (PBBM) is used to elevate drug product quality by providing a more accurate and holistic understanding of how drugs interact with the human body. These models are based on the integration of physiological, pharmacological, and pharmaceutical data to simulate and predict drug behavior in vivo. Effective utilization of PBBM requires a consistent approach to model development, verification, validation, and application. Currently, only one country has a draft guidance document for PBBM, whereas other major regulatory authorities have had limited experience with the review of PBBM. To address this gap, industry submitted confidential PBBM case studies to be reviewed by the regulatory agencies; software companies committed to training. PBBM cases were independently and collaboratively discussed by regulators, and academic colleagues participated in some of the discussions. Successful bioequivalence "safe space" industry case examples are also presented. Overall, six regulatory agencies were involved in the case study exercises, including ANVISA, FDA, Health Canada, MHRA, PMDA, and EMA (experts from Belgium, Germany, Norway, Portugal, Spain, and Sweden), and we believe this is the first time such a collaboration has taken place. The outcomes were presented at this workshop, together with a participant survey on the utility and experience with PBBM submissions, to discuss the best scientific practices for developing, validating, and applying PBBMs. The PBBM case studies enabled industry to receive constructive feedback from global regulators and highlighted clear direction for future PBBM submissions for regulatory consideration.

Effect of Food, Crushing of Tablets, and Antacid Coadministration on Maribavir Pharmacokinetics in Healthy Adult Participants: Results From 2 Phase 1, Open-Label, Randomized, Crossover Studies

The effect of food composition, tablet crushing, and antacid coadministration on maribavir pharmacokinetics was assessed in 2 Phase 1 studies in healthy adults. In the first, a single maribavir 400-mg dose was administered under fasting conditions, with a low-fat/low-calorie or a high-fat/high-calorie meal. In the second, a single maribavir 100-mg dose was administered under fasting conditions, as a crushed tablet, or as a whole tablet alone or with an antacid. The 90% confidence intervals of the geometric mean ratios were within 80%-125% for area under the concentration-time curve (AUC), but not for maximum plasma concentration (Cmax) for low-fat/low-calorie and high-fat/high-calorie meals versus fasting or for whole tablet with antacid versus whole tablet alone. The 90% confidence intervals of the geometric mean ratios for AUC and Cmax were within 80%-125% for crushed versus whole tablet. Maribavir median time to Cmax value in plasma under fed conditions was delayed versus fasting conditions, but there was no statistical difference for crushed versus whole tablet or with versus without antacid. As the antiviral efficacy of maribavir is driven by AUC but not Cmax, findings suggest that maribavir can be administered with food or antacids or as a crushed tablet.

Does Sample Size, Sampling Strategy, or Handling of Concentrations Below the Lower Limit of Quantification Matter When Externally Evaluating Population Pharmacokinetic Models?

BACKGROUND AND OBJECTIVES

Precision dosing requires selecting the appropriate population pharmacokinetic model, which can be assessed through external evaluations (EEs). The lack of understanding of how different study design factors influence EE study outcomes makes it challenging to select the most suitable model for clinical use. This study aimed to evaluate the impact of sample size, sampling strategy, and handling of concentrations below the lower limit of quantification (BLQ) on the outcomes of EE for four population pharmacokinetic models using vancomycin and tobramycin as examples.

METHODS

Three virtual patient populations undergoing vancomycin or tobramycin therapy were simulated with varying sample size and sampling scenarios. The three approaches used to handle BLQ data were to (1) discard them, (2) impute them as LLOQ/2, or (3) use a likelihood-based approach. EEs were performed with NONMEM and R.

RESULTS

Sample size did not have an important impact on the EE results for a given scenario. Increasing the number of samples per patient did not improve predictive performance for two out of the three evaluated models. Evaluating a model developed with rich sampling did not result in better performance than those developed with regular therapeutic drug monitoring. A likelihood-based method to handle BLQ samples impacted the outcomes of the EE with lower bias for predicted troughs.

CONCLUSIONS

This study suggests that a large sample size may not be necessary for an EE study, and models selected based on TDM may be more generalizable. The study highlights the need for guidelines for EE of population pharmacokinetic models for clinical use.

Assessing and mitigating pH-mediated DDI risks in drug development - formulation approaches and clinical considerations

pH-mediated drug-drug interactions (DDI) is a prevalent DDI in drug development, especially for weak base compounds with highly pH-dependent solubility. FDA has released a guidance on the evaluation of pH-mediated DDI assessments using in vitro testing and clinical studies. Currently, there is no common practice of ways of testing across the academia and industry. The development of biopredictive method and physiologically-based biopharmaceutics modeling (PBBM) approaches to assess acid-reducing agent (ARA)-DDI have been proven with accurate prediction and could decrease drug development burden, inform clinical design and potentially waive clinical studies. Formulation strategies and careful clinical design could help mitigate the pH-mediated DDI to avoid more clinical studies and label restrictions, ultimately benefiting the patient. In this review paper, a detailed introduction on biorelevant dissolution testing, preclinical and clinical study requirement and PBPK modeling approaches to assess ARA-DDI are described. An improved decision tree for pH-mediated DDI is proposed. Potential mitigations including clinical or formulation strategies are discussed.

A transformer model for cause-specific hazard prediction

BACKGROUD

Modelling discrete-time cause-specific hazards in the presence of competing events and non-proportional hazards is a challenging task in many domains. Survival analysis in longitudinal cohorts often requires such models; notably when the data is gathered at discrete points in time and the predicted events display complex dynamics. Current models often rely on strong assumptions of proportional hazards, that is rarely verified in practice; or do not handle sequential data in a meaningful way. This study proposes a Transformer architecture for the prediction of cause-specific hazards in discrete-time competing risks. Contrary to Multilayer perceptrons that were already used for this task (DeepHit), the Transformer architecture is especially suited for handling complex relationships in sequential data, having displayed state-of-the-art performance in numerous tasks with few underlying assumptions on the task at hand.

RESULTS

Using synthetic datasets of 2000-50,000 patients, we showed that our Transformer model surpassed the CoxPH, PyDTS, and DeepHit models for the prediction of cause-specific hazard, especially when the proportional assumption did not hold. The error along simulated time outlined the ability of our model to anticipate the evolution of cause-specific hazards at later time steps where few events are observed. It was also superior to current models for prediction of dementia and other psychiatric conditions in the English longitudinal study of ageing cohort using the integrated brier score and the time-dependent concordance index. We also displayed the explainability of our model's prediction using the integrated gradients method.

CONCLUSIONS

Our model provided state-of-the-art prediction of cause-specific hazards, without adopting prior parametric assumptions on the hazard rates. It outperformed other models in non-proportional hazards settings for both the synthetic dataset and the longitudinal cohort study. We also observed that basic models such as CoxPH were more suited to extremely simple settings than deep learning models. Our model is therefore especially suited for survival analysis on longitudinal cohorts with complex dynamics of the covariate-to-outcome relationship, which are common in clinical practice. The integrated gradients provided the importance scores of input variables, which indicated variables guiding the model in its prediction. This model is ready to be utilized for time-to-event prediction in longitudinal cohorts.

Prediction of Pediatric Pharmacokinetics for CYP3A4 Metabolized Drugs: Comparison of the Performance of Two Hepatic Ontogeny Within a Physiologically Based Pharmacokinetic Model

The rapid growth in the use of pediatric physiologically based pharmacokinetic (PBPK) models, particularly for regulatory applications, has focused emphasis on model verification and ensuring system parameters are robust, including how these change with age. Uncertainty remains regarding the ontogeny of some enzymes and transporters, in this study 2 published ontogeny profiles for hepatic CYP3A4 were compared. Clinical pharmacokinetic data on 4 intravenously administered CYP3A4 substrates (alfentanil, fentanyl, midazolam, and sildenafil) used across the pediatric age range was collected from the literature. The PBPK models were verified in the adult population and then used to compare the Salem and a modified Upreti ontogeny profiles for CYP3A4 in terms of parent drug clearance and area under the curve from birth onward. Overall, the modified Upreti ontogeny profile resulted in 15 out of 17 age-related predictions within 2-fold and 12 out of 17 predictions within 1.5-fold ranges of observed values, for the Salem ontogeny these values were 12 out of 17 and 8 out of 17, respectively. The Upreti ontogeny profile performed better than Salem, average fold error and absolute average fold error were 1.14 and 1.35 compared to 1.56 and 1.90, respectively. Identifying the optimal CYP3A4 ontogeny is important for regulatory use of PBPK especially given the number of drugs cleared by this enzyme. This study broadens the evidence from previous studies that Upreti is more favorable than Salem, but further work is needed especially in the neonatal and early infant age range.

Investigational bispecific antibodies for the treatment of rheumatoid arthritis

INTRODUCTION

Rheumatoid arthritis (RA) is an autoimmune disorder with a characteristic chronic inflammation of the synovium that may lead to the destruction of the joints in untreated patients. Interestingly, despite the availability of several effective treatments, many patients do not achieve remission or low disease activity or may experience disease relapse.Following the above unmet needs, bispecific antibodies (BsAbs) have emerged as a new approach to improve the disease's treatment. BsAbs are designed to simultaneously target two different proteins involved in RA pathogenesis, leading to enhanced efficacy and reduced side effects compared to traditional monoclonal antibodies (mAbs).

AREAS COVERED

In this review, we discuss the development of BsAbs for RA treatment, including their mechanism of action, efficacy, and safety profile. We also deal with the challenges and future directions in this field.

EXPERT OPINION

BsAbs show promise in preclinical and clinical evaluations for treating RA. Further research is needed to optimize design and dosage and identify ideal patient groups. BsAbs can benefit disease management and improve outcomes of RA patients.

A machine learning approach to predict daptomycin exposure from two concentrations based on Monte Carlo simulations

Daptomycin is a concentration-dependent lipopeptide antibiotic for which exposure/effect relationships have been shown. Machine learning (ML) algorithms, developed to predict the individual exposure to drugs, have shown very good performances in comparison to maximum a posteriori Bayesian estimation (MAP-BE). The aim of this work was to predict the area under the blood concentration curve (AUC) of daptomycin from two samples and a few covariates using XGBoost ML algorithm trained on Monte Carlo simulations. Five thousand one hundred fifty patients were simulated from two literature population pharmacokinetics models. Data from the first model were split into a training set (75%) and a testing set (25%). Four ML algorithms were built to learn AUC based on daptomycin blood concentration samples at pre-dose and 1 h post-dose. The XGBoost model (best ML algorithm) with the lowest root mean square error (RMSE) in a 10-fold cross-validation experiment was evaluated in both the test set and the simulations from the second population pharmacokinetic model (validation). The ML model based on the two concentrations, the differences between these concentrations, and five other covariates (sex, weight, daptomycin dose, creatinine clearance, and body temperature) yielded very good AUC estimation in the test (relative bias/RMSE = 0.43/7.69%) and validation sets (relative bias/RMSE = 4.61/6.63%). The XGBoost ML model developed allowed accurate estimation of daptomycin AUC using C0, C1h, and a few covariates and could be used for exposure estimation and dose adjustment. This ML approach can facilitate the conduct of future therapeutic drug monitoring (TDM) studies.

Accelerating therapeutics development during a pandemic: population pharmacokinetics of the long-acting antibody combination AZD7442 (tixagevimab/cilgavimab) in the prophylaxis and treatment of COVID-19

AZD7442 is a combination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-neutralizing antibodies, tixagevimab and cilgavimab, developed for pre-exposure prophylaxis (PrEP) and treatment of coronavirus disease 2019 (COVID-19). Using data from eight clinical trials, we describe a population pharmacokinetic (popPK) model of AZD7442 and show how modeling of "interim" data accelerated decision-making during the COVID-19 pandemic. The final model was a two-compartmental distribution model with first-order absorption and elimination, including standard allometric exponents for the effect of body weight on clearance and volume. Other covariates included were as follows: sex, age >65 years, body mass index ≥30 kg/m2, and diabetes on absorption rate; diabetes on clearance; Black race on central volume; and intramuscular (IM) injection site on bioavailability. Simulations indicated that IM injection site and body weight had > 20% effects on AZD7442 exposure, but no covariates were considered to have a clinically relevant impact requiring dose adjustment. The pharmacokinetics of AZD7442, cilgavimab, and tixagevimab were comparable and followed linear kinetics with extended half-lives (median 78.6 days for AZD7442), affording prolonged protection against susceptible SARS-CoV-2 variants. Comparison of popPK simulations based on "interim data" with a target concentration based on 80% viral inhibition and assuming 1.81% partitioning into the nasal lining fluid supported a decision to double the PrEP dosage from 300 mg to 600 mg to prolong protection against Omicron variants. Serum AZD7442 concentrations in adolescents weighing 40-95 kg were predicted to be only marginally different from those observed in adults, supporting authorization for use in adolescents before clinical data were available. In these cases, popPK modeling enabled accelerated clinical decision-making.

Comparison of lumefantrine, mefloquine, and piperaquine concentrations between capillary plasma and venous plasma samples in pregnant women with uncomplicated falciparum and vivax malaria

Capillary samples offer practical benefits compared with venous samples for the measurement of drug concentrations, but the relationship between the two measures varies between different drugs. We measured the concentrations of lumefantrine, mefloquine, piperaquine in 270 pairs of venous plasma and concurrent capillary plasma samples collected from 270 pregnant women with uncomplicated falciparum or vivax malaria. The median and range of venous plasma concentrations included in this study were 447.5 ng/mL (8.81-3,370) for lumefantrine (day 7, n = 76, median total dose received 96.0 mg/kg), 17.9 ng/mL (1.72-181) for desbutyl-lumefantrine, 1,885 ng/mL (762-4,830) for mefloquine (days 3-21, n = 90, median total dose 24.9 mg/kg), 641 ng/mL (79.9-1,950) for carboxy-mefloquine, and 51.8 ng/mL (3.57-851) for piperaquine (days 3-21, n = 89, median total dose 52.2 mg/kg). Although venous and capillary plasma concentrations showed a high correlation (Pearson's correlation coefficient: 0.90-0.99) for all antimalarials and their primary metabolites, they were not directly interchangeable. Using the concurrent capillary plasma concentrations and other variables, the proportions of venous plasma samples predicted within a ±10% precision range was 34% (26/76) for lumefantrine, 36% (32/89) for desbutyl-lumefantrine, 74% (67/90) for mefloquine, 82% (74/90) for carboxy-mefloquine, and 24% (21/89) for piperaquine. Venous plasma concentrations of mefloquine, but not lumefantrine and piperaquine, could be predicted by capillary plasma samples with an acceptable level of agreement. Capillary plasma samples can be utilized for pharmacokinetic and clinical studies, but caution surrounding cut-off values is required at the individual level.CLINICAL TRIALSThis study is registered with ClinicalTrials.gov as NCT01054248.

Mapping stress-responsive signaling pathways induced by mitochondrial proteostasis perturbations

Imbalances in mitochondrial proteostasis are associated with pathologic mitochondrial dysfunction implicated in etiologically diverse diseases. This has led to considerable interest in defining the mechanisms responsible for regulating mitochondria in response to mitochondrial stress. Numerous stress-responsive signaling pathways have been suggested to regulate mitochondria in response to proteotoxic stress. These include the integrated stress response (ISR), the heat shock response (HSR), and the oxidative stress response (OSR). Here, we define the stress signaling pathways activated in response to chronic mitochondrial proteostasis perturbations by monitoring the expression of sets of genes regulated downstream of each of these signaling pathways in published Perturb-seq datasets from K562 cells CRISPRi-depleted of mitochondrial proteostasis factors. Interestingly, we find that the ISR is preferentially activated in response to chronic, genetically-induced mitochondrial proteostasis stress, with no other pathway showing significant activation. Further, we demonstrate that CRISPRi depletion of other mitochondria-localized proteins similarly shows preferential activation of the ISR relative to other stress-responsive signaling pathways. These results both establish our gene set profiling approach as a viable strategy to probe stress responsive signaling pathways induced by perturbations to specific organelles and identify the ISR as the predominant stress-responsive signaling pathway activated in response to chronic disruption of mitochondrial proteostasis.

Modeling the complexity of drug-drug interactions: A physiologically-based pharmacokinetic study of Lenvatinib with Schisantherin A/Schisandrin A

BACKGROUND

Lenvatinib's efficacy as a frontline targeted therapy for radioactive iodine-refractory thyroid carcinoma and advanced hepatocellular carcinoma owes to its inhibition of multiple tyrosine kinases. However, as a CYP3A4 substrate, lenvatinib bears susceptibility to pharmacokinetic modulation by co-administered agents. Schisantherin A (STA) and schisandrin A (SIA) - bioactive lignans abundant in the traditional Chinese medicinal Wuzhi Capsule - act as CYP3A4 inhibitors, engendering the potential for drug-drug interactions (DDIs) with lenvatinib.

METHODS

To explore potential DDIs between lenvatinib and STA/SIA, we developed a physiologically-based pharmacokinetic (PBPK) model for lenvatinib and used it to construct a DDI model for lenvatinib and STA/SIA. The model was validated with clinical trial data and used to predict changes in lenvatinib exposure with combined treatment.

RESULTS

Following single-dose administration, the predicted area under the plasma concentration-time curve (AUC) and maximum plasma concentrations (Cmax) of lenvatinib increased 1.00- to 1.03-fold and 1.00- to 1.01-fold, respectively, in the presence of STA/SIA. Simulations of multiple-dose regimens revealed slightly greater interactions, with lenvatinib AUC0-t and Cmax increasing up to 1.09-fold and 1.02-fold, respectively.

CONCLUSION

Our study developed the first PBPK and DDI models for lenvatinib as a victim drug. STA and SIA slightly increased lenvatinib exposure in simulations, providing clinically valuable information on the safety of concurrent use. Given the minimal pharmacokinetic changes, STA/SIA are unlikely to interact with lenvatinib through pharmacokinetic alterations synergistically but rather may enhance efficacy through inherent anti-cancer efficacy of STA/ SIA.

Non-Labeled, Stable Labeled, or Radiolabelled Approaches for Provision of Intravenous Pharmacokinetics in Humans: A Discussion Piece

A review of the use of microdoses and isotopic microtracers for clinical intravenous pharmacokinetic (i.v. PK) data provision is presented. The extent of application of the varied approaches available and the relative merits of each are highlighted with the aim of assisting practitioners in making informed decisions on the most scientifically appropriate design to adopt for any given new drug in development. It is envisaged that significant efficiencies will be realized as i.v. PK data in humans becomes more routinely available for suitable assets in early development, than has been the case prior to the last decade.

Predicting Pharmacokinetics of Drugs Using Artificial Intelligence Tools: A Systematic Review

BACKGROUND AND OBJECTIVE

Pharmacokinetic studies encompass the examination of the absorption, distribution, metabolism, and excretion of bioactive compounds. The pharmacokinetics of drugs exert a substantial influence on their efficacy and safety. Consequently, the investigation of pharmacokinetics holds great importance. However, laboratory-based assessment necessitates the use of numerous animals, various materials, and significant time. To mitigate these challenges, alternative methods such as artificial intelligence have emerged as a promising approach. This systematic review aims to review existing studies, focusing on the application of artificial intelligence tools in predicting the pharmacokinetics of drugs.

METHODS

A pre-prepared search strategy based on related keywords was used to search different databases (PubMed, Scopus, Web of Science). The process involved combining articles, eliminating duplicates, and screening articles based on their titles, abstracts, and full text. Articles were selected based on inclusion and exclusion criteria. Then, the quality of the included articles was assessed using an appraisal tool.

RESULTS

Ultimately, 23 relevant articles were included in this study. The clearance parameter received the highest level of investigation, followed by the  area under the concentration-time curve (AUC) parameter, in pharmacokinetic studies. Among the various models employed in the articles, Random Forest and eXtreme Gradient Boosting (XGBoost) emerged as the most commonly utilized ones. Generalized Linear Models and Elastic Nets (GLMnet) and Random Forest models showed the most performance in predicting clearance.

CONCLUSION

Overall, artificial intelligence tools offer a robust, rapid, and precise means of predicting various pharmacokinetic parameters based on a dataset containing information of patients or drugs.

Brivaracetam exposure-response predictions in pediatric patients from age 1 month: Extrapolation of levetiracetam adult-pediatric scaling to brivaracetam

BACKGROUND

An adult population pharmacokinetic/pharmacodynamic (PK/PD) model for the antiseizure medication (ASM) brivaracetam (BRV) was previously extended to children aged 4-16 years by using a pediatric BRV population PK model. Effects were scaled using information from a combined adult-pediatric PK/PD model of a related ASM, levetiracetam (LEV).

OBJECTIVE

To scale an existing adult population PK/PD model for BRV to children aged 1 month to < 4 years using information from a combined adult-pediatric PK/PD model for LEV, and to predict the effective dose of BRV in children aged 1 month to < 4 years using the adult BRV PK/PD model modified for the basal seizure rate in children.

MATERIAL AND METHODS

An existing adult population PK/PD model for BRV was scaled to children aged from 1 month to < 4 years using information from a combined adult-pediatric PK/PD model for LEV, an ASM binding to the same target protein as BRV. An existing adult-pediatric PK/PD model for LEV was extended using data from UCB study N01009 (NCT00175890) to include children as young as 1 month of age. The BRV population PK model was updated with data up to 180 days after first administration from BRV pediatric studies N01263 (NCT00422422) and N01266 (NCT01364597). PK and PD simulations for BRV were performed for a range of mg/kg doses to predict BRV effect in pediatric participants, and to provide dosing recommendations.

RESULTS

The extended adult-pediatric LEV PK/PD model was able to describe the adult and pediatric data using the same PD model parameters in adults and children and supported the extension of the adult BRV PK/PD model to pediatric patients aged 1 month to < 4 years. Simulations predicted exposures similar to adults receiving BRV 100 mg twice daily (b.i.d.), when using 3 mg/kg b.i.d. for weight < 10 kg, 2.5 mg/kg b.i.d. for weight ≥ 10 kg and < 20 kg, and 2 mg/kg b.i.d. for weight ≥ 20 kg in children aged 1 month to < 4 years. PK/PD simulations show that maximum BRV response is expected to occur with 2-3 mg/kg b.i.d. dosing of BRV in children aged 1 month to < 4 years, with an effective dose of 1 mg/kg b.i.d. for some participants.

CONCLUSION

Development of an adult-pediatric BRV PK/PD model allowed characterization of the exposure-response relationship of BRV in children aged 1 to < 4 years, providing a maximal dose allowance based on weight.

Metabolite Bioanalysis in Drug Development: Recommendations from the IQ Consortium Metabolite Bioanalysis Working Group

The intent of this perspective is to share the recommendations of the International Consortium for Innovation and Quality in Pharmaceutical Development Metabolite Bioanalysis Working Group on the fit-for-purpose metabolite bioanalysis in support of drug development and registration. This report summarizes the considerations for the trigger, timing, and rigor of bioanalysis in the various assessments to address unique challenges due to metabolites, with respect to efficacy and safety, which may arise during drug development from investigational new drug (IND) enabling studies, and phase I, phase II, and phase III clinical trials to regulatory submission. The recommended approaches ensure that important drug metabolites are identified in a timely manner and properly characterized for efficient drug development.

Development of machine-learning models using pharmacy inquiry database for predicting dose-related inquiries in a tertiary teaching hospital

BACKGROUND

Drug-related problems (DRPs) are a significant concern in healthcare. Pharmacists play a vital role in detecting and resolving DRPs to improve patient safety. A pharmacy inquiry program was established in a tertiary teaching hospital to document inquiries about physicians' orders, aimed at preventing potential DRPs or providing medication information during order reviews.

OBJECTIVE

We aimed to develop machine-learning models using a pharmacy inquiry database to predict dose-related inquiries based on prescriptions and patient information.

METHODS

This retrospective study analyzed 20,393 pharmacy inquiries collected between January 2018 and February 2023. Data included prescription information (drug ingredient, dose, unit, and frequency), patient characteristics (age, sex, weight, and department), and renal function. The inquiries were categorized into two classes: dose-related inquiries (e.g., wrong dose and inappropriate regimen) and non-dose-related inquiries (e.g., inappropriate drug form and administration route). Six machine-learning models were developed: logistic regression, support vector classifier, decision tree, random forest, extreme gradient boosting, and categorical boosting. To evaluate the performance of the models, the area under the receiver operating characteristic curve and the accuracy were compared.

RESULTS

The CatBoost model achieved the highest performance (sensitivity: 0.92; accuracy: 0.79). The SHapley Additive exPlanations values highlighted the importance of features in the model predictions, drug ingredients, units, and renal function, in that order. Notably, lower renal function positively contributed to the prediction of dose-related inquiries. Additionally, the subsequent feature importance among drug ingredients showed that drugs such as acetylsalicylic acid, famotidine, metformin, and spironolactone strongly influenced the prediction of dose-related inquiries.

CONCLUSION

Machine-learning models that use pharmacy inquiry data can effectively predict dose-related inquiries. Further external validation and refinement of the models are required for broader applications in healthcare settings. These findings provide valuable guidance for healthcare professionals and highlight the potential of machine learning in pharmacists' decision-making.

Assessment of the CYP3A4 Induction Potential by Carbamazepine: Insights from Two Clinical DDI Studies and PBPK Modeling

In the past, rifampicin was well-established as strong index CYP3A inducer in clinical drug-drug interaction (DDI) studies. However, due to identified potentially genotoxic nitrosamine impurities, it should not any longer be used in healthy volunteer studies. Available clinical data suggest carbamazepine as an alternative to rifampicin as strong index CYP3A4 inducer in clinical DDI studies. Further, physiologically-based pharmacokinetic (PBPK) modeling is a tool with increasing importance to support the DDI risk assessment of drugs during drug development. CYP3A4 induction properties and the safety profile of carbamazepine were investigated in two open-label, fixed sequence, crossover clinical pharmacology studies in healthy volunteers using midazolam as a sensitive index CYP3A4 substrate. Carbamazepine was up-titrated from 100 mg twice daily (b.i.d.) to 200 mg b.i.d., and to a final dose of 300 mg b.i.d. for 10 consecutive days. Mean area under plasma concentration-time curve from zero to infinity (AUC(0-∞)) of midazolam consistently decreased by 71.8% (ratio: 0.282, 90% confidence interval (CI): 0.235-0.340) and 67.7% (ratio: 0.323, 90% CI: 0.256-0.407) in study 1 and study 2, respectively. The effect was adequately described by an internally developed PBPK model for carbamazepine which has been made freely available to the scientific community. Further, carbamazepine was safe and well-tolerated in the investigated dosing regimen in healthy participants. The results demonstrated that the presented design is appropriate for the use of carbamazepine as alternative inducer to rifampicin in DDI studies acknowledging its CYP3A4 inductive potency and safety profile.

Small molecule drug metabolite synthesis and identification: why, when and how?

The drug discovery and development process encompasses the interrogation of metabolites arising from the biotransformation of drugs. Here we look at why, when and how metabolites of small-molecule drugs are synthesised from the perspective of a specialist contract research organisation, with particular attention paid to projects for which regulatory oversight is relevant during this journey. To illustrate important aspects, we look at recent case studies, trends and learnings from our experience of making and identifying metabolites over the past ten years, along with with selected examples from the literature.

Model-Based Benefit/Risk Analysis for the Copanlisib Intermittent Dosing Regimen

Copanlisib is an intravenously administered phosphatidylinositol 3-kinase (PI3K) inhibitor, which is approved as monotherapy for relapsed follicular lymphoma in adult patients who have received at least two systemic therapies. In an April 2022 US Food and Drug Administration (FDA) Oncology Drug Advisory Committee (ODAC), the benefit-risk profile of the class PI3K inhibitors were scrutinized for use in hematological malignancies. Specifically, their unique toxicities may contribute to the high incidences in reported serious and high-grade treatment emergent adverse events (TEAEs), thereby reducing their overall tolerability and potentially limiting their successful use. These tolerability concerns may be contributed by or compounded by inadequate dose optimization. The recommended dosing regimen of copanlisib 60 mg administered on days 1, 8, and 15 of a 28-day cycle was selected as the maximal tolerated dose (MTD) during phase I. Thus, this analysis sought to justify the copanlisib dose regimen selection. Copanlisib exposure-efficacy relationships were considered from its large phase III trial, CHRONOS-3, whereas copanlisib safety was investigated by pooling data across its two large clinical trials to comprehensively assess its exposure-safety relationships. Results demonstrated a statistically significant positive linear exposure-efficacy relationship at the MTD. Exposure-safety analyses revealed a borderline significant linear relationship for grade ≥3 TEAEs and no significant exposure-safety relationships for other investigated safety end points. The model-based benefit/risk framework considered the established exposure-response models and defined clinical utility function which confirmed the appropriateness of the copanlisib dosing regimen across the range of its achieved exposures.

A modeling analysis of whole body potassium regulation on a high-potassium diet: proximal tubule and tubuloglomerular feedback effects

Potassium (K+) is an essential electrolyte that plays a key role in many physiological processes, including mineralcorticoid action, systemic blood-pressure regulation, and hormone secretion and action. Indeed, maintaining K+ balance is critical for normal cell function, as too high or too low K+ levels can have serious and potentially deadly health consequences. K+ homeostasis is achieved by an intricate balance between the intracellular and extracellular fluid as well as balance between K+ intake and excretion. This is achieved via the coordinated actions of regulatory mechanisms such as the gastrointestinal feedforward effect, insulin and aldosterone upregulation of Na+-K+-ATPase uptake, and hormone and electrolyte impacts on renal K+ handling. We recently developed a mathematical model of whole body K+ regulation to unravel the individual impacts of these regulatory mechanisms. In this study, we extend our mathematical model to incorporate recent experimental findings that showed decreased fractional proximal tubule reabsorption under a high-K+ diet. We conducted model simulations and sensitivity analyses to investigate how these renal alterations impact whole body K+ regulation. Model predictions quantify the sensitivity of K+ regulation to various levels of proximal tubule K+ reabsorption adaptation and tubuloglomerular feedback. Our results suggest that the reduced proximal tubule K+ reabsorption under a high-K+ diet could achieve K+ balance in isolation, but the resulting tubuloglomerular feedback reduces filtration rate and thus K+ excretion.NEW & NOTEWORTHY Potassium homeostasis is maintained in the body by a complex system of regulatory mechanisms. This system, when healthy, maintains a small extracellular potassium concentration, despite large fluctuations of dietary potassium. The complexities of the system make this problem well suited for investigation with mathematical modeling. In this study, we extend our mathematical model to consider recent experimental results on renal potassium handling on a high potassium diet and investigate the impacts from a whole body perspective.

Population pharmacokinetic-pharmacodynamic model of subcutaneous bupivacaine in a novel extended-release microparticle formulation

The objective of this study was to develop a population pharmacokinetic-pharmacodynamic model of subcutaneously administered bupivacaine in a novel extended-release microparticle formulation for postoperative pain management. Bupivacaine was administered subcutaneously in the lower leg to 28 healthy male subjects in doses from 150 to 600 mg in a phase 1 randomized, placebo-controlled, double-blind, dose-ascending study with two different microparticle formulations, LIQ865A and LIQ865B. Warmth detection threshold was used as a surrogate pharmacodynamic endpoint. Population pharmacokinetic-pharmacodynamic models were fitted to plasma concentration-effect-time data using non-linear mixed-effects modelling. The pharmacokinetics were best described by a two-compartment model with biphasic absorption as two parallel absorption processes: a fast, zero-order process and a slower, first-order process with two transit compartments. The slow absorption process was found to be dose-dependent and rate-limiting for elimination at higher doses. Apparent bupivacaine clearance and the transit rate constant describing the slow absorption process both appeared to decrease with increasing doses following a power function with a shared covariate effect. The pharmacokinetic-pharmacodynamic relationship between plasma concentrations and effect was best described by a linear function. This model gives new insight into the pharmacokinetics and pharmacodynamics of microparticle formulations of bupivacaine and the biphasic absorption seen for several local anaesthetics.

A physiologically-based pharmacokinetic precision dosing approach to manage dasatinib drug-drug interactions

Dasatinib, a second-generation tyrosine kinase inhibitor, is approved for treating chronic myeloid and acute lymphoblastic leukemia. As a sensitive cytochrome P450 (CYP) 3A4 substrate and weak base with strong pH-sensitive solubility, dasatinib is susceptible to enzyme-mediated drug-drug interactions (DDIs) with CYP3A4 perpetrators and pH-dependent DDIs with acid-reducing agents. This work aimed to develop a whole-body physiologically-based pharmacokinetic (PBPK) model of dasatinib to describe and predict enzyme-mediated and pH-dependent DDIs, to evaluate the impact of strong and moderate CYP3A4 inhibitors and inducers on dasatinib exposure and to support optimized dasatinib dosing. Overall, 63 plasma profiles from perorally administered dasatinib in healthy volunteers and cancer patients were used for model development. The model accurately described and predicted plasma profiles with geometric mean fold errors (GMFEs) for area under the concentration-time curve from the first to the last timepoint of measurement (AUClast) and maximum plasma concentration (Cmax) of 1.27 and 1.29, respectively. Regarding the DDI studies used for model development, all (8/8) predicted AUClast and Cmax ratios were within twofold of observed ratios. Application of the PBPK model for dose adaptations within various DDIs revealed dasatinib dose reductions of 50%-80% for strong and 0%-70% for moderate CYP3A4 inhibitors and a 2.3-3.1-fold increase of the daily dasatinib dose for CYP3A4 inducers to match the exposure of dasatinib administered alone. The developed model can be further employed to personalize dasatinib therapy, thereby help coping with clinical challenges resulting from DDIs and patient-related factors, such as elevated gastric pH.

The effect of AZD9567 vs. prednisolone on glycaemic control in patients with type 2 diabetes mellitus: Results from a phase 2a clinical trial

AIMS

Corticosteroids are the treatment of choice for many inflammatory diseases but often lead to adverse effects, including hyperglycaemia. This study investigated the mechanisms driving differential effects on glucose control for AZD9567, an oral nonsteroidal selective glucocorticoid receptor modulator vs. prednisolone in 46 patients with type 2 diabetes mellitus.

METHODS

In this randomized, double-blind, 2-way cross-over study (NCT04556760), participants received either AZD9567 72 mg and prednisolone 40 mg daily (cohort 1); AZD9567 40 mg and prednisolone 20 mg daily (cohort 2); or placebo and prednisolone 5 mg daily (cohort 3). Treatment duration was 3 days with a 3-week washout between treatment periods. Glycaemic control was assessed after a standardized meal and with continuous glucose monitoring.

RESULTS

A significant difference between AZD9567 and prednisolone in favour of AZD9567 was observed for the change from baseline to Day 4 glucose excursions postmeal in cohort 1 (glucose area under the curve from 0 to 4 h -4.54%; 95% confidence interval [CI]: -8.88, -0.01; P = .049), but not in cohort 2 (-5.77%; 95% CI: -20.92, 12.29; P = .435). In cohort 1, significant differences between AZD9567 and prednisolone were also seen for the change from baseline to day 4 in insulin and glucagon secretion postmeal (P < .001 and P = .005, respectively) and change from baseline to Day 4 in GLP-1 response (P = .022). Significant differences between AZD9567 and prednisolone for 24-h glucose control were observed for both cohort 1 (-1.507 mmol/L; 95% CI: -2.0820, -0.9314; P < .001) and cohort 2 (-1.110 mmol/L; 95% CI -1.7257, -0.4941; P < .001).

CONCLUSION

AZD9567 significantly reduced treatment-induced hyperglycaemia compared with prednisolone.

Different states of synaptic vesicle priming explain target cell type-dependent differences in neurotransmitter release

Pronounced differences in neurotransmitter release from a given presynaptic neuron, depending on the synaptic target, are among the most intriguing features of cortical networks. Hippocampal pyramidal cells (PCs) release glutamate with low probability to somatostatin expressing oriens-lacunosum-moleculare (O-LM) interneurons (INs), and the postsynaptic responses show robust short-term facilitation, whereas the release from the same presynaptic axons onto fast-spiking INs (FSINs) is ~10-fold higher and the excitatory postsynaptic currents (EPSCs) display depression. The mechanisms underlying these vastly different synaptic behaviors have not been conclusively identified. Here, we applied a combined functional, pharmacological, and modeling approach to address whether the main difference lies in the action potential-evoked fusion or else in upstream priming processes of synaptic vesicles (SVs). A sequential two-step SV priming model was fitted to the peak amplitudes of unitary EPSCs recorded in response to complex trains of presynaptic stimuli in acute hippocampal slices of adult mice. At PC-FSIN connections, the fusion probability (Pfusion) of well-primed SVs is 0.6, and 44% of docked SVs are in a fusion-competent state. At PC-O-LM synapses, Pfusion is only 40% lower (0.36), whereas the fraction of well-primed SVs is 6.5-fold smaller. Pharmacological enhancement of fusion by 4-AP and priming by PDBU was recaptured by the model with a selective increase of Pfusion and the fraction of well-primed SVs, respectively. Our results demonstrate that the low fidelity of transmission at PC-O-LM synapses can be explained by a low occupancy of the release sites by well-primed SVs.

Prediction of Individual Disease Progression Including Parameter Uncertainty in Rare Neurodegenerative Diseases: The Example of Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS)

The aim of this study was to develop a model to predict individual subject disease trajectories including parameter uncertainty and accounting for missing data in rare neurological diseases, showcased by the ultra-rare disease Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS). We modelled the change in SARA (Scale for Assessment and Rating of Ataxia) score versus Time Since Onset of symptoms using non-linear mixed effect models for a population of 173 patients with ARSACS included in the prospective real-world multicenter Autosomal Recessive Cerebellar Ataxia (ARCA) registry. We used the Multivariate Imputation Chained Equation (MICE) algorithm to impute missing covariates, and a covariate selection procedure with a pooled p-value to account for the multiply imputed data sets. We then investigated the impact of covariates and population parameter uncertainty on the prediction of the individual trajectories up to 5 years after their last visit. A four-parameter logistic function was selected. Men were estimated to have a 25% lower SARA score at disease onset and a moderately higher maximum SARA score, and time to progression (T50) was estimated to be 35% lower in patients with age of onset over 15 years. The population disease progression rate started slowly at 0.1 points per year peaking to a maximum of 0.8 points per year (at 36.8 years since onset of symptoms). The prediction intervals for SARA scores 5 years after the last visit were large (median 7.4 points, Q1-Q3: 6.4-8.5); their size was mostly driven by individual parameter uncertainty and individual disease progression rate at that time.

Dose Optimization Informed by PBPK Modeling: State-of-the Art and Future

Model-informed drug development (MIDD) is a powerful quantitative approach that plays an integral role in drug development and regulatory review. While applied throughout the life cycle of the development of new drugs, a key application of MIDD is to inform clinical trial design including dose selection and optimization. To date, physiologically-based pharmacokinetic (PBPK) modeling, an established component of the MIDD toolkit, has mainly been used for assessment of drug-drug interactions (DDIs) and consequential dose adjustments in regulatory submissions. As a result of recent scientific advances and growing confidence in the utility of the approach, PBPK models are being increasingly applied to provide dose recommendations for subjects with differing ages, genetics, and disease states. In this review, we present our perspective on the current landscape of regulatory acceptance of PBPK applications supported by relevant case studies. We also discuss the recent progress and future challenges associated with expanding the utility of PBPK models into emerging areas for regulatory decision making, especially dose optimization in highly vulnerable and understudied populations and facilitating diversity in clinical trials.

Why the racing industry and equestrian disciplines need to implement population pharmacokinetics: To learn, explain, summarize, harmonize, and individualize

Population pharmacokinetics (POP PK) is a powerful pharmacokinetic tool, which measures quantitatively, and explains the variability in drug exposure and drug effect between individuals. POP PK uses an observational (nonexperimental) approach; it is conducted in the target population living in its normal environment (e.g., farm and race-track). The strength of the POP PK approach lies in its greater relevance for the population studied in its different natural environments than experimental studies carried out in more or less biased laboratory conditions. In clinical settings, it is commonly necessary to restrict the number of samples per subject collected for analysis and the derived data cannot be analyzed using traditional individual data analytical methods; rather data are merged and analyzed with an appropriate statistical tool: the nonlinear mixed effect model (NLMEM). POP PK modeling is frequently used with the objective of adjusting drug dosage, and hence drug exposure, not only for the whole population but also for subgroups of animals (e.g., for a given breed, sex, and age). It can also have application at the individual subject level, in the context of precision medicine. For horses, the use of the POP PK/PD model will allow prescribers to estimate an individual Withdrawal Time for a given horse whose treatment they are supervising. Another potential field of application will be meta-analysis of existing data to generate new knowledge on a drug or to collate and synthesize, in an objective and transparent manner, existing data; this will facilitate harmonization of screening limits at an international level.

"Getting the Dose Right"-Revisiting the Topic With Focus on Biologic Agents

Nearly two decades after the Peck and Cross article '"Getting the dose right: facts, a blueprint, and encouragements" was published, a review of dose recommendations for biologics shows that the success in getting the dose right appears to have improved given the relatively low incidence of drug withdrawals and dosing/label changes. However, the clinical experience with monoclonal antibodies (MAbs) following approval has been less than perfect. In inflammatory diseases, the disease burden changes with time and high treatment failure rates have been reported. In addition, the use of concomitant steroids and immunosuppressant drugs with MAbs is common. These concomitant agents have their own safety issues and many immunosuppressant agents are not well-tolerated although they have been shown to reduce the incidence of anti-drug antibodies (ADA). This same complexity is seen in MAbs used in oncology as well, although with these agents the doses appear to be higher than needed, which results in high treatment costs and incidence of adverse events. Given the complexity of MAb pharmacokinetics, which makes providing a detailed description of dose options difficult, product labeling should include the options for alternative dose strategies and potentially include the use of therapeutic drug monitoring with dose individualization which have been shown to improve clinical response and reduce the incidence of ADA. So, while the recommended dosing for biologics seems improved over the issues noted 17 years ago, we still have some work to do.

Viral Kinetics Model of SARS-CoV-2 Infection Informs Drug Discovery, Clinical Dose, and Regimen Selection

Quantitative systems pharmacology (QSP) has been an important tool to project safety and efficacy of novel or repurposed therapies for the SARS-CoV-2 virus. Here, we present a QSP modeling framework to predict response to antiviral therapeutics with three mechanisms of action (MoA): cell entry inhibitors, anti-replicatives, and neutralizing biologics. We parameterized three distinct model structures describing virus-host interaction by fitting to published viral kinetics data of untreated COVID-19 patients. The models were used to test theoretical behaviors and map therapeutic design criteria of the different MoAs, identifying the most rapid and robust antiviral activity from neutralizing biologic and anti-replicative MoAs. We found good agreement between model predictions and clinical viral load reduction observed with anti-replicative nirmatrelvir/ritonavir (Paxlovid®) and neutralizing biologics bamlanivimab and casirivimab/imdevimab (REGEN-COV®), building confidence in the modeling framework to inform a dose selection. Finally, the model was applied to predict antiviral response with ensovibep, a novel DARPin therapeutic designed as a neutralizing biologic. We developed a new in silico measure of antiviral activity, area under the curve (AUC) of free spike protein concentration, as a metric with larger dynamic range than viral load reduction. By benchmarking to bamlanivimab predictions, we justified dose levels of 75, 225, and 600 mg ensovibep to be administered intravenously in a Phase 2 clinical investigation. Upon trial completion, we found model predictions to be in good agreement with the observed patient data. These results demonstrate the utility of this modeling framework to guide the development of novel antiviral therapeutics.

The foundation for investigating factor XI as a target for inhibition in human cardiovascular disease

Anticoagulant therapy is a mainstay in the management of patients with cardiovascular disease and related conditions characterized by a heightened risk for thrombosis. Acute coronary syndrome, chronic coronary syndrome, ischemic stroke, and atrial fibrillation are the most common. In addition to their proclivity for thrombosis, each of these four conditions is also characterized by local and systemic inflammation, endothelial/endocardial injury and dysfunction, oxidative stress, impaired tissue-level reparative capabilities, and immune dysregulation that plays a critical role in linking molecular events, environmental triggers, and phenotypic expressions. Knowing that cardiovascular disease and thrombosis are complex and dynamic, can the scientific community identify a common pathway or specific point of interface susceptible to pharmacological inhibition or alteration that is likely to be safe and effective? The contact factors of coagulation may represent the proverbial "sweet spot" and are worthy of investigation. The following review provides a summary of the fundamental biochemistry of factor XI, its biological activity in thrombosis, inflammation, and angiogenesis, new targeting drugs, and a pragmatic approach to managing hemostatic requirements in clinical trials and possibly day-to-day patient care in the future.

A Phase 1a Study to Evaluate Safety, Tolerability, Pharmacokinetics, and Pharmacodynamics of RO7303509, an Anti-TGFβ3 Antibody, in Healthy Volunteers

INTRODUCTION

Transforming growth factor beta (TGFβ) cytokines (TGFβ1, TGFβ2, and TGFβ3) play critical roles in tissue fibrosis. However, treatment with systemic pan-TGFβ inhibitors have demonstrated unacceptable toxicities. In this study, we evaluated the safety, tolerability, pharmacokinetics, and pharmacodynamics of RO7303509, a high-affinity, TGFβ3-specific, humanized immunoglobulin G1 monoclonal antibody, in healthy adult volunteers (HVs).

METHODS

This phase 1a, randomized, double-blind trial included six cohorts for evaluation, with each cohort receiving single doses of placebo or RO7303509, administered intravenously (IV; 50 mg, 150 mg, 240 mg) or subcutaneously (SC; 240 mg, 675 mg, 1200 mg). The frequency and severity of adverse events (AEs) and RO7303509 serum concentrations were monitored throughout the study. We also measured serum periostin and cartilage oligomeric matrix protein (COMP) by immunoassay and developed a population pharmacokinetics model to characterize RO7303509 serum concentrations.

RESULTS

The study enrolled 49 HVs, with a median age of 39 (range 18-73) years. Ten (27.8%) RO7303509-treated subjects reported 24 AEs, and six (30.8%) placebo-treated subjects reported six AEs. The most frequent AEs related to the study drug were injection site reactions and infusion-related reactions. Maximum serum concentrations (Cmax) and area under the concentration-time curve from time 0 to infinity (AUC0-inf) values for RO7303509 appeared to increase dose-proportionally across all doses tested. Serum concentrations across cohorts were best characterized by a two-compartment model plus a depot compartment with first-order SC absorption kinetics. No subjects tested positive for anti-drug antibodies (ADAs) at baseline; one subject (2.8%; 50 mg IV) tested positive for ADAs at a single time point (day 15). No clear pharmacodynamic effects were observed for periostin or COMP upon TGFβ3 inhibition.

CONCLUSION

RO7303509 was well tolerated at single SC doses up to 1200 mg in HVs with favorable pharmacokinetic data that appeared to increase dose-proportionally. TGFβ3-specific inhibition may be suitable for development as a chronic antifibrotic therapy.

TRIAL REGISTRATION

ISRCTN13175485.

Evaluation of ChatGPT and Gemini large language models for pharmacometrics with NONMEM

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.

Quantification of biochemical PSA dynamics after radioligand therapy with [177Lu]Lu-PSMA-I&T using a population pharmacokinetic/pharmacodynamic model

BACKGROUND

There is an unmet need for prediction of treatment outcome or patient selection for [177Lu]Lu-PSMA therapy in patients with metastatic castration-resistant prostate cancer (mCRPC). Quantification of the tumor exposure-response relationship is pivotal for further treatment optimization. Therefore, a population pharmacokinetic (PK) model was developed for [177Lu]Lu-PSMA-I&T using SPECT/CT data and, subsequently, related to prostate-specific antigen (PSA) dynamics after therapy in patients with mCRPC using a pharmacokinetic/pharmacodynamic (PKPD) modelling approach.

METHODS

A population PK model was developed using quantitative SPECT/CT data (406 scans) of 76 patients who received multiple cycles [177Lu]Lu-PSMA-I&T (± 7.4 GBq with either two- or six-week interval). The PK model consisted of five compartments; central, salivary glands, kidneys, tumors and combined remaining tissues. Covariates (tumor volume, renal function and cycle number) were tested to explain inter-individual variability on uptake into organs and tumors. The final PK model was expanded with a PD compartment (sequential fitting approach) representing PSA dynamics during and after treatment. To explore the presence of a exposure-response relationship, individually estimated [177Lu]Lu-PSMA-I&T tumor concentrations were related to PSA changes over time.

RESULTS

The population PK model adequately described observed data in all compartments (based on visual inspection of goodness-of-fit plots) with adequate precision of parameters estimates (< 36.1% relative standard error (RSE)). A significant declining uptake in tumors (k14) during later cycles was identified (uptake decreased to 73%, 50% and 44% in cycle 2, 3 and 4-7, respectively, compared to cycle 1). Tumor growth (defined by PSA increase) was described with an exponential growth rate (0.000408 h-1 (14.2% RSE)). Therapy-induced PSA decrease was related to estimated tumor concentrations (MBq/L) using both a direct and delayed drug effect. The final model adequately captured individual PSA concentrations after treatment (based on goodness-of-fit plots). Simulation based on the final PKPD model showed no evident differences in response for the two different dosing regimens currently used.

CONCLUSIONS

Our population PK model accurately described observed [177Lu]Lu-PSMA-I&T uptake in salivary glands, kidneys and tumors and revealed a clear declining tumor uptake over treatment cycles. The PKPD model adequately captured individual PSA observations and identified population response rates for the two dosing regimens. Hence, a PKPD modelling approach can guide prediction of treatment response and thus identify patients in whom radioligand therapy is likely to fail.

Leveraging QSP Models for MIPD: A Case Study for Warfarin/INR

Warfarin dosing remains challenging due to substantial inter-individual variability, which can lead to unsafe or ineffective therapy with standard dosing. Model-informed precision dosing (MIPD) can help individualize warfarin dosing, requiring the selection of a suitable model. For models developed from clinical data, the dependence on the study design and population raises questions about generalizability. Quantitative system pharmacology (QSP) models promise better extrapolation abilities; however, their complexity and lack of validation on clinical data raise questions about applicability in MIPD. We have previously derived a mechanistic warfarin/international normalized ratio (INR) model from a blood coagulation QSP model. In this article, we evaluated the predictive performance of the warfarin/INR model in the context of MIPD using an external dataset with INR data from patients starting warfarin treatment. We assessed the accuracy and precision of model predictions, benchmarked against an empirically based reference model. Additionally, we evaluated covariate contributions and assessed the predictive performance separately in the more challenging outpatient data. The warfarin/INR model performed comparably to the reference model across various measures despite not being calibrated with warfarin initiation data. Including CYP2C9 and/or VKORC1 genotypes as covariates improved the prediction quality of the warfarin/INR model, even after assimilating 4 days of INR data. The outpatient INR exhibited higher unexplained variability, and predictions slightly exceeded observed values, suggesting that model adjustments might be necessary when transitioning from an inpatient to an outpatient setting. Overall, this research underscores the potential of QSP-derived models for MIPD, offering a complementary approach to empirical model development.

Antibody drug conjugates: Design implications for clinicians

OBJECTIVE

There are currently 11 antibody-drug conjugates (ADC) that are FDA approved for use in oncologic disease states, with many more in the pipeline. The authors aim to review the pharmacokinetic profiles of the components of ADCs to engage pharmacist practitioners in practical considerations in the care of patients. This article provides an overview on the use of ADCs in the setting of organ dysfunction, drug-drug interactions, and management of on- and off-target adverse effects.

DATA SOURCES

A systematic search of the literature on ADCs through September 2023 was conducted. Clinical trials as well as articles on ADC design and functional components, adverse effects, and pharmacokinetics were reviewed. Reviewed literature included prescribing information as well as tertiary sources and primary literature.

DATA SUMMARY

A total of 11 ADCs were reviewed for the purpose of this article. A description of the mechanism of action and structure of ADCs is outlined, and a table containing description of each currently FDA-approved ADC is included. Various mechanisms of ADC toxicity are reviewed, including how ADC structure may be implicated.

CONCLUSION

It is imperative that pharmacist clinicians understand the design and function of each component of an ADC to continue to assess new approvals for use in oncology patients. Understanding the design of the ADC can help a pharmacy practitioner compare and contrast adverse effect profiles to support their multidisciplinary teams and to engage patients in education and management of their care.

De-escalation of Biologic Treatment in Inflammatory Bowel Disease: A Comprehensive Review

INTRODUCTION

Biologic therapy is an effective treatment for inflammatory bowel disease [IBD]. However due to cost and safety concerns, dose de-escalation strategies after achieving remission have been suggested.

AIM

To critically review available data on dose de-escalation of biologics [or other advanced therapies] in IBD. We will focus on studies evaluating de-escalation to standard dosing in patients initially optimised, and also on studies assessing de-escalation from standard dosing.

METHODS

A systematic bibliographic search was performed.

RESULTS

The mean frequency of de-escalation after previous dose intensification [12 studies, 1,474 patients] was 34%. The corresponding frequency of de-escalation from standard dosing [five studies, 3,842 patients] was 4.2%. The relapse rate of IBD following anti-tumour necrosis factor [TNF] de-escalation to standard dosing in patients initially dose-escalated [10 studies, 301 patients] was 30%. The corresponding relapse rate following anti-TNF de-escalation from standard dosing [nine studies, 494 patients] was 38%. The risk of relapse was lower for patients in clinical, biologic, and endoscopic/radiological remission at the time of de-escalation. A role of anti-TNF therapeutic drug monitoring in the decision to dose de-escalate has been demonstrated. In patients relapsing after de-escalation, re-escalation is generally effective. De-escalation is not consistently associated with a better safety profile. The cost-effectiveness of the de-escalation strategy remains uncertain. Finally, there is not enough evidence to recommend dose de-escalation of biologics different from anti-TNFs or small molecules.

CONCLUSIONS

Any consideration for de-escalation of biologic therapy in IBD must be tailored, taking into account the risks and consequences of a flare and patients' preferences.

Exploring the Impact of Hepatic Impairment on Pralsetinib Pharmacokinetics

Pralsetinib is a kinase inhibitor indicated for the treatment of metastatic rearranged during transfection (RET) fusion-positive non-small cell lung cancer. Pralsetinib is primarily eliminated by the liver and hence hepatic impairment (HI) is likely alter its pharmacokinetics (PK). Mild HI has been shown to have minimal impact on the PK of pralsetinib. This hepatic impairment study aimed to determine the pralsetinib PK, safety and tolerability in subjects with moderate and severe HI, as defined by the Child-Pugh and National Cancer Institute Organ Dysfunction Working Group (NCI-ODWG) classification systems, in comparison to subjects with normal hepatic function. Based on the Child-Pugh classification, subjects with moderate and severe HI had similar systemic exposure (area under the plasma concentration time curve from time 0 to infinity [AUC0-∞]) to pralsetinib, with AUC0-∞ geometric mean ratios (GMR) of 1.12 and 0.858, respectively, compared to subjects with normal hepatic function. Results based on the NCI-ODWG classification criteria were comparable; the AUC0-∞ GMR were 1.22 and 0.858, respectively, for subjects with moderate and severe HI per NCI-ODWG versus those with normal hepatic function. These results suggested that moderate and severe hepatic impairment did not have a meaningful impact on the exposure to pralsetinib, thus not warranting a dose adjustment in this population.

Development of Novel Methods for QSAR Modeling by Machine Learning Repeatedly: A Case Study on Drug Distribution to Each Tissue

Artificial intelligence is expected to help identify excellent candidates in drug discovery. However, we face a lack of data, as it is time-consuming and expensive to acquire raw data perfectly for many compounds. Hence, we tried to develop a novel quantitative structure-activity relationship (QSAR) method to predict a parameter more precisely from an incomplete data set via optimizing data handling by making use of predicted explanatory variables. As a case study we focused on the tissue-to-plasma partition coefficient (Kp), which is an important parameter for understanding drug distribution in tissues and building the physiologically based pharmacokinetic model and is a representative of small and sparse data sets. In this study, we predicted the Kp values of 119 compounds in nine tissues (adipose, brain, gut, heart, kidney, liver, lung, muscle, and skin), although some of these were not available. To fill the missing values in Kp for each tissue, first we predicted those Kp values by the nonmissing data set using a random forest (RF) model with in vitro parameters (log P, fu, Drug Class, and fi) like a classical prediction by a QSAR model. Next, to predict the tissue-specific Kp values in a test data set, we constructed a second RF model with not only in vitro parameters but also the Kp values of other tissues (i.e., other than target tissues) predicted by the first RF model as explanatory variables. Furthermore, we tested all possible combinations of explanatory variables and selected the model with the highest predictability from the test data set as the final model. The evaluation of Kp prediction accuracy based on the root-mean-square error and R2 value revealed that the proposed models outperformed other machine learning methods such as the conventional RF and message-passing neural networks. Significant improvements were observed in the Kp values of adipose tissue, brain, kidney, liver, and skin. These improvements indicated that the Kp information on other tissues can be used to predict the same for a specific tissue. Additionally, we found a novel relationship between each tissue by evaluating all combinations of explanatory variables. In conclusion, we developed a novel RF model to predict Kp values. We hope that this method will be applied to various problems in the field of experimental biology which often contains missing values in the near future.

Plasmodium falciparum Alba6 exhibits DNase activity and participates in stress response

Alba domain proteins, owing to their functional plasticity, play a significant role in organisms. Here, we report an intrinsic DNase activity of PfAlba6 from Plasmodium falciparum, an etiological agent responsible for human malignant malaria. We identified that tyrosine28 plays a critical role in the Mg2+ driven 5'-3' DNase activity of PfAlba6. PfAlba6 cleaves both dsDNA as well as ssDNA. We also characterized PfAlba6-DNA interaction and observed concentration-dependent oligomerization in the presence of DNA, which is evident from size exclusion chromatography and single molecule AFM-imaging. PfAlba6 mRNA expression level is up-regulated several folds following heat stress and treatment with artemisinin, indicating a possible role in stress response. PfAlba6 has no human orthologs and is expressed in all intra-erythrocytic stages; thus, this protein can potentially be a new anti-malarial drug target.

Selection of a Suitable Animal Model to Evaluate Secretion of Drugs in the Human Milk: A Systematic Approach

Lack of data on drug secretion in human milk is a concern for safe use of drugs during postpartum.Clinical studies are often difficult to perform; despite substantial improvements in computational methodologies such as physiologically based pharmacokinetic modelling, there is limited clinical data to validate such models for many drugs.Various factors that are likely to impact milk to plasma ratio were identified. A literature search was performed to gather available data on milk composition, total volume of milk produced per day, milk pH, haematocrit, and renal blood flow and glomerular filtration rate in various animal models.BLAST nucleotide and protein tools were used to evaluate the similarities between humans and animals in the expression and predominance of selected drug transporters, metabolic enzymes, and blood proteins.A multistep analysis of all the potential variables affecting drug secretion was considered to identify most appropriate animal model. The practicality of using the animal in a lab setting was also considered.Donkeys and goats were identified as the most suitable animals for studying drug secretion in milk.

Modeling and simulation for prediction of multiple sclerosis progression

In light of extensive work that has created a wide range of techniques for predicting the course of multiple sclerosis (MS) disease, this paper attempts to provide an overview of these approaches and put forth an alternative way to predict the disease progression. For this purpose, the existing methods for estimating and predicting the course of the disease have been categorized into clinical, radiological, biological, and computational or artificial intelligence-based markers. Weighing the weaknesses and strengths of these prognostic groups is a profound method that is yet in need and works directly at the level of diseased connectivity. Therefore, we propose using the computational models in combination with established connectomes as a predictive tool for MS disease trajectories. The fundamental conduction-based Hodgkin-Huxley model emerged as promising from examining these studies. The advantage of the Hodgkin-Huxley model is that certain properties of connectomes, such as neuronal connection weights, spatial distances, and adjustments of signal transmission rates, can be taken into account. It is precisely these properties that are particularly altered in MS and that have strong implications for processing, transmission, and interactions of neuronal signaling patterns. The Hodgkin-Huxley (HH) equations as a point-neuron model are used for signal propagation inside a small network. The objective is to change the conduction parameter of the neuron model, replicate the changes in myelin properties in MS and observe the dynamics of the signal propagation across the network. The model is initially validated for different lengths, conduction values, and connection weights through three nodal connections. Later, these individual factors are incorporated into a small network and simulated to mimic the condition of MS. The signal propagation pattern is observed after inducing changes in conduction parameters at certain nodes in the network and compared against a control model pattern obtained before the changes are applied to the network. The signal propagation pattern varies as expected by adapting to the input conditions. Similarly, when the model is applied to a connectome, the pattern changes could give an insight into disease progression. This approach has opened up a new path to explore the progression of the disease in MS. The work is in its preliminary state, but with a future vision to apply this method in a connectome, providing a better clinical tool.

Hierarchical cluster analysis and nonlinear mixed-effects modelling for candidate biomarker detection in preclinical models of cancer

BACKGROUND

Preclinical models of cancer can be of translational benefit when assessing how different biomarkers are regulated in response to particular treatments. Detection of molecular biomarkers in preclinical models of cancer is difficult due inter-animal variability in responses, combined with limited accessibility of longitudinal data.

METHODS

Nonlinear mixed-effects modelling (NLME) was used to analyse tumour growth data based on expected tumour growth rates observed 7 days after initial doses (DD7) of Radiotherapy (RT) and Combination of RT with DNA Damage Response Inhibitors (DDRi). Cox regression was performed to confirm an association between DD7 and survival. Hierarchical Cluster Analysis (HCA) was then used to identify candidate biomarkers impacting responses to RT and RT/DDRi and these were validated using NLME.

RESULTS

Cox regression confirmed significant associations between DD7 and survival. HCA of RT treated samples, combined with NLME confirmed significant associations between DD7 and Cluster specific CD8+ Ki67 MFI, as well as DD7 and cluster specific Natural Killer cell density in RT treated mice.

CONCLUSION

Application of NLME, as well as HCA of candidate biomarkers may provide additional avenues to assess the effect of RT in MC38 syngeneic tumour models. Additional studies would need to be conducted to confirm association between DD7 and biomarkers in RT/DDRi treated mice.

Bioequivalence risk assessment of oral formulations containing racemic ibuprofen through a chiral physiologically based pharmacokinetic model of ibuprofen enantiomers

The characterization of the time course of ibuprofen enantiomers can be useful in the selection of the most sensitive analyte in bioequivalence studies. Physiologically based pharmacokinetic (PBPK) modelling and simulation represents the most efficient methodology to virtually assess bioequivalence outcomes. In this work, we aim to develop and verify a PBPK model for ibuprofen enantiomers administered as a racemic mixture with different immediate release dosage forms to anticipate bioequivalence outcomes based on different particle size distributions. A PBPK model incorporating stereoselectivity and non-linearity in plasma protein binding and metabolism as well as R-to-S unidirectional inversion has been developed in Simcyp®. A dataset composed of 11 Phase I clinical trials with 54 scenarios (27 per enantiomer) and 14,452 observations (7129 for R-ibuprofen and 7323 for S-ibuprofen) was used. Prediction errors for AUC0-t and Cmax for both enantiomers fell within the 0.8-1.25 range in 50/54 (93 %) and 42/54 (78 %) of scenarios, respectively. Outstanding model performance, with 10/10 (100 %) of Cmax and 9/10 (90 %) of AUC0-t within the 0.9-1.1 range, was demonstrated for oral suspensions, which strongly supported its use for bioequivalence risk assessment. The deterministic bioequivalence risk assessment has revealed R-ibuprofen as the most sensitive analyte to detect differences in particle size distribution for oral suspensions containing 400 mg of racemic ibuprofen, suggesting that achiral bioanalytical methods would increase type II error and declare non-bioequivalence for formulations that are bioequivalent for the eutomer.

Stereoretentive enantioconvergent reactions

Enantioconvergent reactions are pre-eminent in contemporary asymmetric synthesis as they convert both enantiomers of a racemic starting material into a single enantioenriched product, thus avoiding the maximum 50% yield associated with resolutions. All currently known enantioconvergent processes necessitate the loss or partial loss of the racemic substrate's stereochemical information, thus limiting the potential substrate scope to molecules that contain labile stereogenic units. Here we present an alternative approach to enantioconvergent reactions that can proceed with full retention of the racemic substrate's configuration. This uniquely stereo-economic approach is possible if the two enantiomers of a racemic starting material are joined together to form one enantiomer of a non-meso product. Experimental validation of this concept is presented using two distinct strategies: (1) a direct asymmetric coupling approach, and (2) a multicomponent approach, which exhibits statistical amplification of enantiopurity. Thus, the established dogma that enantioconvergent reactions require substrates that contain labile stereogenic units is shown to be incorrect.