In silico platform for xenobiotics ADME-T pharmacological properties modeling and prediction. Part I: Beyond the reduction of animal model use

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Two sigma and two mu class genes of glutathione S-transferase in the waterflea Daphnia pulex: Molecular characterization and transcriptional response to nanoplastic exposure

Two isoforms of Glutathione S-Transferase (GST) genes, belonging to mu (Dp-GSTm1 and Dp-GSTm2) and sigma (Dp-GSTs1 and Dp-GSTs2) classes, were cloned and characterised in the freshwater Daphnia pulex. No signal peptide was found in any of the four GST proteins, indicating that they were cytosolic GST. A highly conserved glutathione (GSH) binding site (G-site) occurred in the N-terminal sequence, and a substrate binding site (H-site), interacting non-specifically with the second hydrophobic substrate, was present in the C-terminal. A Tyr residue, for the stabilization of GSH, was found to be conserved in the analysed sequences. The secondary and tertiary structures indicated that these genes possess the typical cytosolic GST structure, including a conserved N-terminal domain with a βαβαββα motif. The μ loop (NVGPAPDYDR and NFIGAEWDR in Dp-GSTm1 and Dp-GSTm2, respectively) was identified between the βαβ (β1α1β2) and αββα motifs (α2β3β4α3) in the N-terminal domain. The expressions of Dp-GSTs1, Dp-GSTs2, and Dp-GSTm1 were higher in other age groups compared to the newly-born neonates (1 d); however, the expression of Dp-GSTm2 first increased and then decreased with age. Gene expression was significantly reduced by high concentration (1 and 2 mg/L) of 75 nm polystyrene nanoplastic. However, nanoplastic exposure at the predicted environmental concentration (1 μg/L) had a low effect. Exposure of mothers to nanoplastic (1 μg/L) elevated the Dp-GSTs2 level in their neonates. These results improve our understanding on the response of different types of Daphnid GST to environmental contaminants, especially nanoplastic.

Current advances in mathematical modeling of anti-cancer drug penetration into tumor tissues

Delivery of anti-cancer drugs to tumor tissues, including their interstitial transport and cellular uptake, is a complex process involving various biochemical, mechanical, and biophysical factors. Mathematical modeling provides a means through which to understand this complexity better, as well as to examine interactions between contributing components in a systematic way via computational simulations and quantitative analyses. In this review, we present the current state of mathematical modeling approaches that address phenomena related to drug delivery. We describe how various types of models were used to predict spatio-temporal distributions of drugs within the tumor tissue, to simulate different ways to overcome barriers to drug transport, or to optimize treatment schedules. Finally, we discuss how integration of mathematical modeling with experimental or clinical data can provide better tools to understand the drug delivery process, in particular to examine the specific tissue- or compound-related factors that limit drug penetration through tumors. Such tools will be important in designing new chemotherapy targets and optimal treatment strategies, as well as in developing non-invasive diagnosis to monitor treatment response and detect tumor recurrence.

The role of tumor tissue architecture in treatment penetration and efficacy: an integrative study

Despite the great progress that has been made in understanding cancer biology and the potential molecular targets for its treatment, the majority of drugs fail in the clinical trials. This may be attributed (at least in part) to the complexity of interstitial drug transport in the patient’s body, which is hard to test experimentally. Similarly, recent advances in molecular imaging have led to the development of targeted biomarkers that can predict pharmacological responses to therapeutic interventions. However, both the drug and biomarker molecules need to access the tumor tissue and be taken up into individual cells in concentrations sufficient to exert the desired effect. To investigate the process of drug penetration at the mesoscopic level we developed a computational model of interstitial transport that incorporates the biophysical properties of the tumor tissue, including its architecture and interstitial fluid flow, as well as the properties of the agents. This model is based on the method of regularized Stokeslets to describe the fluid flow coupled with discrete diffusion-advection-reaction equations to model the dynamics of the drugs. Our results show that the tissue cellular porosity and density influence the depth of penetration in a non-linear way, with sparsely packed tissues being traveled through more slowly than the denser tissues. We demonstrate that irregularities in the cell spatial configurations result in the formation of interstitial corridors that are followed by agents leading to the emergence of tissue zones with less exposure to the drugs. We describe how the model can be integrated with in vivo experiments to test the extravasation and penetration of the targeted biomarkers through the tumor tissue. A better understanding of tissue- or compound-specific factors that limit the penetration through the tumors is important for non-invasive diagnoses, chemotherapy, the monitoring of treatment responses, and the detection of tumor recurrence.

PaDEL-DDPredictor: open-source software for PD-PK-T prediction

ADMET (absorption, distribution, metabolism, excretion, and toxicity)-related failure of drug candidates is a major issue for the pharmaceutical industry today. Prediction of PD-PK-T properties using in silico tools has become very important in pharmaceutical research to reduce cost and enhance efficiency. PaDEL-DDPredictor is an in silico tool for rapid prediction of PD-PK-T properties of compounds from their chemical structures. It is free and open-source software that, has both graphical user interface and command line interface, can work on all major platforms (Windows, Linux, and MacOS) and supports more than 90 different molecular file formats. The software can be downloaded from http://padel.nus.edu.sg/software/padelddpredictor.

Use of mathematical models to understand anticancer drug delivery and its effect on solid tumors

The transport of anticancer drugs and their effect on tumor cells involve a number of physical and biochemical processes. Mathematical modeling provides a tool to help us understand the interaction of these complex processes, thereby contributing to the improvement and optimization of drug delivery. This article starts with a discussion of the biological and physiological properties of tumors, which are often found as barriers to anticancer drug transport and effect. A broad spectrum of mathematical models is reviewed to give an overview of the current state of modeling approaches and different categories of models are outlined. These include pharmacokinetic and transport-based models for the prediction of temporal and temporal-spatial profiles of antidrug concentrations, as well as empirical or deterministic models to describe the effect of drug. We conclude that the systematic elucidation and integration of cellular signal transduction with the biophysical aspects of drug transport will lead to a better understanding of the entire drug-delivery process.

Some Trends in Chem(o)informatics

This introductory chapter gives a brief overview of the history of cheminformatics, and then summarizes some recent trends in computing, cultures, open systems, chemical structure representation, docking, de novo design, fragment-based drug design, molecular similarity, quantitative structure-activity relationships (QSAR), metabolite prediction, the use of phamacophores in drug discovery, data reduction and visualization, and text mining. The aim is to set the scene for the more detailed exposition of these topics in the later chapters.

Estimation of ADME properties with substructure pattern recognition

Over the past decade, absorption, distribution, metabolism, and excretion (ADME) property evaluation has become one of the most important issues in the process of drug discovery and development. Since in vivo and in vitro evaluations are costly and laborious, in silico techniques had been widely used to estimate ADME properties of chemical compounds. Traditional prediction methods usually try to build a functional relationship between a set of molecular descriptors and a given ADME property. Although traditional methods have been successfully used in many cases, the accuracy and efficiency of molecular descriptors must be concerned. Herein, we report a new classification method based on substructure pattern recognition, in which each molecule is represented as a substructure pattern fingerprint based on a predefined substructure dictionary, and then a support vector machine (SVM) algorithm is applied to build the prediction model. Therefore, a direct connection between substructures and molecular properties is built. The most important substructure patterns can be identified via the information gain analysis, which could help to interpret the models from a medicinal chemistry perspective. Afterward, this method was verified with two data sets, one for blood-brain barrier (BBB) penetration and the other for human intestinal absorption (HIA). The results demonstrated that the overall predictive accuracies of the best HIA model for the training and test sets were 98.5 and 98.8%, and the overall predictive accuracies of the best BBB model for the training and test sets were 98.8 and 98.4%, which confirmed the reliability of our method. In the additional validations, the predictive accuracies were 94 and 69.5% for the HIA and the BBB models, respectively. Moreover, some of the representative key substructure patterns which significantly correlated with the HIA and BBB penetration properties were also presented.