Dose-fractionation studies of a Plasmodium phosphatidylinositol 4-kinase inhibitor in a humanized mouse model of malaria.

UCT594 is a 2-aminopyrazine carboxylic acid Plasmodium phosphatidylinositol 4-kinase inhibitor with potent asexual blood-stage activity, the potential for interrupting transmission, as well as liver-stage activities. Herein, we investigated pharmacokinetic/pharmacodynamic (PK/PD) relationships relative to blood-stage activity toward predicting the human dose. Dose-fractionation studies were conducted in the Plasmodium falciparum NSG mouse model to determine the PK/PD indices of UCT594, using the in vivo minimum parasiticidal concentration as a threshold. UCT594 demonstrated concentration-dependent killing in the P. falciparum-infected NSG mouse model. Using this data and the preclinical pharmacokinetic data led to a low predicted human dose of <50 mg. This makes UCT594 an attractive potential antimalarial drug.

Integrating Pharmacokinetic-Pharmacodynamic Modeling and Physiologically Based Pharmacokinetic Modeling to Optimize Human Dose Predictions for Plasmodium falciparum Malaria: a Chloroquine Case Study.

The translation of a preclinical antimalarial drug development candidate to the clinical phases should be supported by rational human dose selection. A model-informed strategy based on preclinical data, which incorporates pharmacokinetic-pharmacodynamic (PK-PD) properties with physiologically based pharmacokinetic (PBPK) modeling, is proposed to optimally predict an efficacious human dose and dosage regimen for the treatment of Plasmodium falciparum malaria. The viability of this approach was explored using chloroquine, which has an extensive clinical history for malaria treatment. First, the PK-PD parameters and the PK-PD driver of efficacy for chloroquine were determined through a dose fractionation study in the P. falciparum-infected humanized mouse model. A PBPK model for chloroquine was then developed for predicting the drug's PK profiles in a human population, from which the human PK parameters were determined. Lastly, the PK-PD parameters estimated in the P. falciparum-infected mouse model and the human PK parameters derived from the PBPK model were integrated to simulate the human dose-response relationships against P. falciparum, which subsequently allowed the determination of an optimized treatment. The predicted efficacious human dose and dosage regimen for chloroquine were comparable to those recommended clinically for the treatment of uncomplicated, drug-sensitive malaria, which provided supportive evidence for the proposed model-based approach to antimalarial human dose predictions.

Target-mediated drug disposition modeling of an anti-TFPI antibody (MG1113) in cynomolgus monkeys to predict human pharmacokinetics and pharmacodynamics.

BACKGROUND: MG1113 is a human monoclonal antibody of tissue factor pathway inhibitor (TFPI) under development for prophylaxis for hemophilia patients with or without inhibitors against factor VIII products, which have been used for the treatment of hemophilia. Because TFPI is a negative regulator in the extrinsic coagulation pathway, neutralization of TFPI function by MG1113 can potentially increase coagulation activity by bypassing the intrinsic coagulation pathway, which factor VIII activates. OBJECTIVES: This study aims to determine the correlation between pharmacokinetics (PK) and pharmacodynamics (PD) after administering MG1113 to monkeys and to predict the PK and PD of MG1113 in humans by the Target-Mediated Drug Disposition (TMDD) model using the results from monkeys. METHODS: The PK profile of MG1113 and the PD effect on the free TFPI level were evaluated after intravenous (IV) and subcutaneous (SC) administrations of MG1113 (2.5, 5, and 10 mg/kg) to male cynomolgus monkeys. After setting up the PK/PD model on monkeys, PK parameters on humans were calculated using allometric scaling, and then clinically effective doses were predicted applying the TMDD model. RESULTS AND CONCLUSIONS: MG1113 showed nonlinear PK after both IV and SC administrations at the dosing range from 2.5 to10 mg/kg. The concentrations of MG1113 versus TFPI could be characterized a dose-response relationship using a TMDD model. The TMDD modeling and simulation built in this study were used to simulate various dosage regimens of MG1113 to apply to the first-in-human study design, and moreover expected to be referred to establish the dose for further clinical trials.

Ethambutol disposition in humans: Challenges and limitations of whole-body physiologically-based pharmacokinetic modelling in early drug development.

Whole-body physiologically based pharmacokinetic (WB-PBPK) models have become an important tool in drug development, as they enable characterization of pharmacokinetic profiles across different organs based on physiological (systems-specific) and physicochemical (drug-specific) properties. However, it remains unclear which data are needed for accurate predictions when applying the approach to novel candidate molecules progressing into the clinic. In this work, as case study, we investigated the predictive performance of WB-PBPK models both for prospective and retrospective evaluation of the pharmacokinetics of ethambutol, considering scenarios that reflect different stages of development, including settings in which the data are limited to in vitro experiments, in vivo preclinical data, and when some clinical data are available. Overall, the accuracy of PBPK model-predicted systemic and tissue exposure was heavily dependant on prior knowledge about the eliminating organs. Whilst these findings may be specific to ethambutol, the challenges and potential limitations identified here may be relevant to a variety of drugs, raising questions about (1) the minimum requirements for prospective use of WB-PBPK models during the characterization of drug disposition and (2) implication of uncertainty for dose selection in humans.

ADME: Assessing Pharmacokinetic-Pharmacodynamic Parameters of Oligonucleotides.

We describe tactics to assess pharmacokinetic (PK) and pharmacodynamic (PD) parameters of oligonucleotides. The chapter includes recommendations on the design of single-dose preclinical PK studies, preclinical PKPD studies, and toxicological studies, and on best practice for scaling PK and PD parameters from animal to human. We focus on single-stranded oligonucleotides, but relevant differences to double-stranded RNAs are also addressed.

UCT943, a Next-Generation Plasmodium falciparum PI4K Inhibitor Preclinical Candidate for the Treatment of Malaria.

The 2-aminopyridine MMV048 was the first drug candidate inhibiting Plasmodium phosphatidylinositol 4-kinase (PI4K), a novel drug target for malaria, to enter clinical development. In an effort to identify the next generation of PI4K inhibitors, the series was optimized to improve properties such as solubility and antiplasmodial potency across the parasite life cycle, leading to the 2-aminopyrazine UCT943. The compound displayed higher asexual blood stage, transmission-blocking, and liver stage activities than MMV048 and was more potent against resistant Plasmodium falciparum and Plasmodium vivax clinical isolates. Excellent in vitro antiplasmodial activity translated into high efficacy in Plasmodium berghei and humanized P. falciparum NOD-scid IL-2Rγ null mouse models. The high passive permeability and high aqueous solubility of UCT943, combined with low to moderate in vivo intrinsic clearance, resulted in sustained exposure and high bioavailability in preclinical species. In addition, the predicted human dose for a curative single administration using monkey and dog pharmacokinetics was low, ranging from 50 to 80 mg. As a next-generation Plasmodium PI4K inhibitor, UCT943, based on the combined preclinical data, has the potential to form part of a single-exposure radical cure and prophylaxis (SERCaP) to treat, prevent, and block the transmission of malaria.

Automatically updating predictive modeling workflows support decision-making in drug design.

Using predictive models for early decision-making in drug discovery has become standard practice. We suggest that model building needs to be automated with minimum input and low technical maintenance requirements. Models perform best when tailored to answering specific compound optimization related questions. If qualitative answers are required, 2-bin classification models are preferred. Integrating predictive modeling results with structural information stimulates better decision making. For in silico models supporting rapid structure-activity relationship cycles the performance deteriorates within weeks. Frequent automated updates of predictive models ensure best predictions. Consensus between multiple modeling approaches increases the prediction confidence. Combining qualified and nonqualified data optimally uses all available information. Dose predictions provide a holistic alternative to multiple individual property predictions for reaching complex decisions.

Automated analysis of routinely generated preclinical pharmacokinetic and pharmacodynamic data.

Model-based analysis of routinely generated pharmacokinetic and pharmacodynamic (PK-PD) data is a key component of preclinical drug discovery. The work process of such analyses can be automated by properly designed computer programs that reduce the number of manual steps, resulting in time saving and significantly fewer errors. Critical decisions can still be made by modelers. Using concrete animal data examples this paper illustrates when, and demonstrates how, automated PK-PD approaches can be used and what benefits they offer to the modeling and simulation community. Specifically, we describe two compound optimization case studies from drug discovery projects, and also demonstrate how a subsequent optimization step to predict the human dose can be coupled to an automated approach.