A New Version of the Tissue Composition-Based Model for Improving the Mechanism-Based Prediction of Volume of Distribution at Steady-State for Neutral Drugs.

In-vitro models are available in the literature for predicting the volume of distribution at steady-state (Vdss) of drugs. The mechanistic model refers to the tissue composition-based model (TCM), which includes important factors that govern Vdss such as drug physiochemistry and physiological data. The recognized TCM published by Rodgers and Rowland (TCM-RR) and a subsequent adjustment made by Simulations Plus Inc. (TCM-SP) have been shown to be generally less accurate with neutral compared to ionized drugs. Therefore, improving these models for neutral drugs becomes necessary. The objective of this study was to propose a new TCM for improving the prediction of Vdss for neutral drugs. The new TCM included two modifications of the published models (i) accentuate the effect of the blood-to-plasma ratio (BPR) that should cover permeated molecules across the biomembranes, which is lacking in these models for neutral compounds, and (ii) use a different approach to estimate the binding in tissues. The new TCM was validated with a large dataset of 202 commercial and proprietary compounds including preclinical and clinical data. All scenario datasets were predicted more accurately with the TCM-New, whereas all statistical parameters indicate that the TCM-New showed significant improvements in terms of accuracy over the TCM-RR and TCM-SP. Predictions of Vdss were frequently more accurate for the TCM-new with 83% within twofold error versus only 50% for the TCM-RR. And more than 95% of the predictions were within threefold error and patient interindividual differences can be predicted with the TCM-New, greatly exceeding the accuracy of the published models. Overall, the new TCM incorporating BPR significantly improved the Vdss predictions in animals and humans for neutral drugs, and, hence, has the potential to better support the drug discovery and facilitate the first-in-human predictions.

Development of a three-step-based novel strategy integrating DMPK with network pharmacology and bioactivity evaluation for the discovery of Q-markers of traditional Chinese medicine prescriptions: Danlou tablet as an example.

BACKGROUND: Quality marker (Q-marker) serves an important role in promoting the standardization of the quality of traditional Chinese medicine (TCM) prescriptions. However, discovering comprehensive and representative Q-markers from TCM prescriptions composed of multiple components remains difficult. PURPOSE: A three-step-based novel strategy integrating drug metabolism and pharmacokinetics (DMPK) with network pharmacology and bioactivity evaluation was proposed to discover the Q-markers and applied to a research example of Danlou tablet (DLT), a famous TCM prescription with remarkable and reliable clinical effects for coronary heart disease (CHD). METHODS: Firstly, the metabolic profile in vivo of DLT was systemically characterized, and the pharmacokinetic (PK) properties of PK markers were then investigated. Secondly, an integrated network of "PK markers - CHD targets - pathways - therapeutic effects" was established to screen out the crucial PK markers of DLT against CHD. Thirdly, the crucial PK markers that could exhibit strong myocardial protection activity in the H9c2 cardiomyocyte model were selected as the candidate Q-markers of DLT. According to the proportion of their Cmax value in vivo, the candidate Q-markers were configured into a composition; the bioactivity was then evaluated to confirm their synergistic effect and justify their usage as Q-markers. RESULTS: First of all, a total of 110 DLT-related xenobiotics (35 prototypes and 75 metabolites) were detected in bio-samples, and the pharmacokinetic properties of 13 PK markers of DLT were successfully characterized, revealing the quality transitivity and traceability from prescription to in vivo. Then, 6 crucial PK markers with three topological features (degree, betweenness, and closeness) greater than the average values in the pharmacology network were screened out as the key components of DLT against CHD. Furthermore, among these 6 crucial PK markers, 5 components (puerarin, alisol A, daidzein, paeoniflorin, and tanshinone IIA) with strong myocardial protection activity were chosen as the candidate Q-markers to constitute a new composition. The composition activated the expression of the PI3K/AKT pathway and exhibited strong myocardial protection activity, and the effective concentrations (nM level) of these components in the composition were significantly lower than their individually effective concentrations (µM level), indicating that there was a certain synergistic effect between them. Hence, the 5 components with multiple properties, including testability, quality transitivity and traceability from prescription to in vivo, effectiveness, and compatibility contribution, were defined as comprehensive and representative Q-markers of DLT. CONCLUSION: This study not only presented a novel idea for the revelation of comprehensive and representative Q-markers in quality control research of TCM prescriptions, but also identified the reasonable Q-markers of DLT for the first time to improve the quality control level of DLT.

OptiMal-PK: an internet-based, user-friendly interface for the mathematical-based design of optimized anti-malarial treatment regimens.

BACKGROUND: The search for highly effective anti-malarial therapies has gathered pace and recent years have seen a number of promising single and combined therapies reach the late stages of development. A key drug development challenge is the need for early assessment of the clinical utility of new drug leads as it is often unclear for developers whether efforts should be focused on efficacy or metabolic stability/exposure or indeed whether the continuation of iterative QSAR (quantitative structure-activity and relationships) cycles of medicinal chemistry and biological testing will translate to improved clinical efficacy. Pharmacokinetic and pharmacodynamic (PK/PD)-based measurements available from in vitro studies can be used for such clinical predictions. However, these predictions often require bespoke mathematical PK/PD modelling expertise and are normally performed after candidate development and, therefore, not during the pre-clinical development phase when such decisions need to be made. METHODS: An internet-based tool has been developed using STELLA(®) software. The tool simulates multiple differential equations that describe anti-malarial PK/PD relationships where the user can easily input PK/PD parameters. The tool utilizes a simple stop-light system to indicate the efficacy of each combination of parameters. This tool, called OptiMal-PK, additionally allows for the investigation of the effect of drug combinations with known or custom compounds. RESULTS: The results of simulations obtained from OptiMal-PK were compared to a previously published and validated mathematical model on which this tool is based. The tool has also been used to simulate the PK/PD relationship for a number of existing anti-malarial drugs in single or combined treatment. Simulations were predictive of the published clinical parasitological clearance activities for these existing therapies. CONCLUSIONS: OptiMal-PK is designed to be implemented by medicinal chemists and pharmacologists during the pre-clinical anti-malarial drug development phase to explore the impact of different PK/PD parameters upon the predicted clinical activity of any new compound. It can help investigators to identify which pharmacological features of a compound are most important to the clinical performance of a new chemical entity and how partner drugs could potentially improve the activity of existing therapies.

Use of 'dilute-and-shoot' liquid chromatography-high resolution mass spectrometry in preclinical research: application to a DMPK study of perhexiline in mouse plasma.

This work describes a simple, sensitive and rapid liquid chromatography-high resolution mass spectrometry method for the quantitation of perhexiline and the simultaneous detection of perhexiline metabolites in C57bl/6 mice plasma. Only 5 µL of plasma was used for analysis. Pretreatment was limited to a 100-fold dilution ('dilute-and-shoot'). The analyte was detected by high resolution mass spectrometry (Orbitrap™ technology). Three scan events were performed over the entire chromatogram. Targeted single ion monitoring with data dependent acquisition was employed for perhexiline quantitation and confirmation, while full scan was used to perform untargeted detection of perhexiline phase I and phase II circulating metabolites. The calibration curve was linear (r(2)=0.990) ranging from 0.305 ng/mL (LLOQ) to 10000 ng/mL. Matrix effect was limited to 6.1%. The method was applied to a pharmacokinetic study of perhexiline in mouse plasma and the results obtained were compared to a standard sample preparation method based on protein precipitation and liquid chromatography-tandem mass spectrometry (MRM mode) detection. The new approach provided comparable results in terms of pharmacokinetics parameters estimate with a high sensitivity, additional information on perhexiline circulating metabolites and a low consumption of biological sample. The combination of the 'dilute-and-shoot' approach together with HRMS targeted and untargeted detection represents a suitable alternative to classic bioanalytical approaches in preclinical research.

Time dependent analysis of assay comparability: a novel approach to understand intra- and inter-site variability over time.

We demonstrate here a novel use of statistical tools to study intra- and inter-site assay variability of five early drug metabolism and pharmacokinetics in vitro assays over time. Firstly, a tool for process control is presented. It shows the overall assay variability but allows also the following of changes due to assay adjustments and can additionally highlight other, potentially unexpected variations. Secondly, we define the minimum discriminatory difference/ratio to support projects to understand how experimental values measured at different sites at a given time can be compared. Such discriminatory values are calculated for 3 month periods and followed over time for each assay. Again assay modifications, especially assay harmonization efforts, can be noted. Both the process control tool and the variability estimates are based on the results of control compounds tested every time an assay is run. Variability estimates for a limited set of project compounds were computed as well and found to be comparable. This analysis reinforces the need to consider assay variability in decision making, compound ranking and in silico modeling.