PK/PD model-informed dose selection for oncology phase I expansion: Case study based on PF-06939999, a PRMT5 inhibitor.

The optimal dose for targeted oncology therapeutics is often not the maximum tolerated dose. Pharmacokinetic/pharmacodynamic (PK/PD) modeling can be an effective tool to integrate clinical data to help identify the optimal dose. This case study shows the utility of population PK/PD modeling in selecting the recommended dose for expansion (RDE) for the first-in-patient (FIP) study of PF-06939999, a small-molecule inhibitor of protein arginine methyltransferase 5.

Joint Disposition Properties and Comprehensive Pharmacokinetic Characterization of Antibody-Drug Conjugates.

Antibody-drug conjugates (ADCs) comprise 3 distinct parts: a specific antibody carrier (mAb), a linker, and a cytotoxic payload. Typical pharmacokinetic (PK) characterization of ADCs remains fragmented using separate noncompartmental analyses (NCA) of individual analytes, offering little insight into the dynamic relationships among the ADC components, and the safety and efficacy implications. As a result, it is exceedingly difficult to compare ADCs in terms of favorable PK characteristics.

Risk-Based Pharmacokinetic and Drug-Drug Interaction Characterization of Antibody-Drug Conjugates in Oncology Clinical Development: An International Consortium for Innovation and Quality in Pharmaceutical Development Perspective.

Antibody-drug conjugates (ADCs) represent a rapidly evolving area of drug development and hold significant promise. To date, nine ADCs have been approved by the US Food and Drug Administration (FDA). These conjugates combine the target specificity of monoclonal antibodies with the anticancer activity of small-molecule therapeutics (also referred to as payload).

Ligand-based and structure-based investigation for Alzheimer's disease from traditional chinese medicine.

Alzheimer's disease is a neurodegenerative disease that was conventionally thought to be related to the sedimentation of beta-amyloids, but drugs designed according to this hypothesis have generally failed. That FKBP52 can reduce the accumulation of tau proteins, and that Tacrolimus can reduce the pathological changes of tau proteins are new directions away from the long held amyloid-beta-centric concept. Therefore, the screening of traditional Chinese medicine compounds for those with higher affinity towards FKBP52 than Tacrolimus may be a new direction for treating Alzheimer's disease.

Bayesian analysis of a rat formaldehyde DNA-protein cross-link model.

As the initial effort in a multi-step uncertainty analysis of a biologically based cancer model for formaldehyde, a Markov chain Monte Carlo (MCMC) analysis was performed for a compartmental model that predicts DNA-protein cross-links (DPX) produced by formaldehyde exposure. The Bayesian approach represented by the MCMC analysis integrates existing knowledge of the model parameters with observed, formaldehyde-DPX-specific data, providing a statistically sound basis for estimating model output uncertainty. Uncertainty and variability were evaluated through a hierarchical structure, where interindividual variability was considered for all model parameters and that variability was assumed to be uncertain on population levels.

Assessing kinetic determinants for metabolism and oral uptake of octamethylcyclotetrasiloxane (D4) from inhalation chamber studies.

The pharmacokinetics of octamethylcyclotetrasiloxane (D4), a highly lipophilic and well-metabolized volatile cyclic siloxane, are more complex than those of other volatile hydrocarbons. The purpose of the present study was to evaluate rate constants for saturable metabolism in the body, to estimate possible presystemic D4 clearance by respiratory-tract tissues, and to assess rate constants for uptake of D4 after oral dosing. These experiments provided the opportunity to refine current physiologically based pharmacokinetic (PBPK) models for D4 and to independently estimate key model parameters by sensitive inhalation methods.

Bayesian estimation of pharmacokinetic and pharmacodynamic parameters in a mode-of-action-based cancer risk assessment for chloroform.

Chloroform is a carcinogen in rodents and its carcinogenicity is secondary to events associated with cytotoxicity and regenerative cell proliferation. In this study, a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model that links the processes of chloroform metabolism, reparable cell damage, cell death, and regenerative cellular proliferation was developed to support a new cancer dose-response assessment for chloroform. Model parameters were estimated using Markov Chain Monte Carlo (MCMC) analysis in a two-step approach: (1) metabolism parameters for male and female mice and rats were estimated against available closed chamber gas uptake data; and (2) PD parameters for each of the four rodent groups were estimated from hepatic and renal labeling index data following inhalation exposures.

Development of a screening approach to interpret human biomonitoring data on volatile organic compounds: reverse dosimetry on biomonitoring data for trichloroethylene.

A screening approach is developed for volatile organic compounds (VOCs) to estimate exposures that correspond to levels measured in fluids and/or tissues in human biomonitoring studies. The approach makes use of a generic physiologically-based pharmacokinetic (PBPK) model coupled with exposure pattern characterization, Monte Carlo analysis, and quantitative structure property relationships (QSPRs). QSPRs are used for VOCs with minimal data to develop chemical-specific parameters needed for the PBPK model.

Reverse dosimetry: interpreting trihalomethanes biomonitoring data using physiologically based pharmacokinetic modeling.

Biomonitoring data provide evidence of exposure of environmental chemicals but are not, by themselves, direct measures of exposure. To use biomonitoring data in understanding exposure, physiologically based pharmacokinetic (PBPK) modeling can be used in a reverse dosimetry approach to assess a distribution of exposures possibly associated with specific blood or urine levels of compounds. Reverse dosimetry integrates PBPK modeling with exposure pattern characterization, Monte Carlo analysis, and statistical tools to estimate a distribution of exposures that are consistent with biomonitoring data in a population.

Use of a physiologically based pharmacokinetic model to identify exposures consistent with human biomonitoring data for chloroform.

Biomonitoring data provide evidence of human exposure to environmental chemicals by quantifying the chemical or its metabolite in a biological matrix. To better understand the correlation between biomonitoring data and environmental exposure, physiologically based pharmacokinetic (PBPK) modeling can be of use. The objective of this study was to use a combined PBPK model with an exposure model for showering to estimate the intake concentrations of chloroform based on measured blood and exhaled breath concentrations of chloroform.

Dose-incidence modeling: consequences of linking quantal measures of response to depletion of critical tissue targets.

In developing mechanistic PK-PD models, incidence of toxic responses in a population has to be described in relation to measures of biologically effective dose (BED). We have developed a simple dose-incidence model that links incidence with BED for compounds that cause toxicity by depleting critical cellular target molecules. The BED in this model was the proportion of target molecule adducted by the dose of toxic compound.

A novel, sensitive method for determining benzo[a]pyrene-diones using high-performance liquid chromatography with post-column zinc reduction.

A novel and sensitive high-performance liquid chromatography (HPLC) method was developed to analyze dione metabolites of benzo[a]pyrene (BaP). Because BaP-diones do not fluoresce, detection of low concentrations is difficult to achieve when analyzing these chemicals with a simple HPLC system. We developed a method to increase the detection sensitivities for BaP-diones using reduction by zinc after the chromatographic separation.

Chemical mixture toxicology: from descriptive to mechanistic, and going on to in silico toxicology.

Because of the pioneering vision of certain leaders in the biomedical field, the last two decades witnessed rapid advances in the area of chemical mixture toxicology. Earlier studies utilized conventional toxicology protocol and methods, and they were mainly descriptive in nature. Two good examples might be the parallel series of studies conducted by the U.

BioMOL: a computer-assisted biological modeling tool for complex chemical mixtures and biological processes at the molecular level.

A chemical engineering approach for the rigorous construction, solution, and optimization of detailed kinetic models for biological processes is described. This modeling capability addresses the required technical components of detailed kinetic modeling, namely, the modeling of reactant structure and composition, the building of the reaction network, the organization of model parameters, the solution of the kinetic model, and the optimization of the model. Even though this modeling approach has enjoyed successful application in the petroleum industry, its application to biomedical research has just begun.

Application of biologically based computer modeling to simple or complex mixtures.

The complexity and the astronomic number of possible chemical mixtures preclude any systematic experimental assessment of toxicology of all potentially troublesome chemical mixtures. Thus, the use of computer modeling and mechanistic toxicology for the development of a predictive tool is a promising approach to deal with chemical mixtures. In the past 15 years or so, physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling has been applied to the toxicologic interactions of chemical mixtures.