Chapter 10 Computational Prediction of ADMET Properties: Recent Developments and Future Challenges

Exploring marine toxins: comparative analysis of chemical reactivity properties and potential for drug discovery

Marine toxins, produced by various marine microorganisms, pose significant risks to both marine ecosystems and human health. Understanding their diverse structures and properties is crucial for effective mitigation and exploration of their potential as therapeutic agents. This study presents a comparative analysis of two hydrophilic and two lipophilic marine toxins, examining their reactivity properties and bioavailability scores. By investigating similarities among these structurally diverse toxins, valuable insights into their potential as precursors for novel drug development can be gained. The exploration of lipophilic and hydrophilic properties in drug design is essential due to their distinct implications on drug distribution, elimination, and target interaction. By elucidating shared molecular properties among toxins, this research aims to identify patterns and trends that may guide future drug discovery efforts and contribute to the field of molecular toxinology. The findings from this study have the potential to expand knowledge on toxins, facilitate a deeper understanding of their bioactivities, and unlock new therapeutic possibilities to address unmet biomedical needs. The results showcased similarities among the studied systems, while also highlighting the exceptional attributes of Domoic Acid (DA) in terms of its interaction capabilities and stability.

Preliminary modelling as the first stage of targeted organic synthesis

The review aims to present a classification and applicability analysis of methods for preliminary molecular modelling for targeted organic, catalytic and biocatalytic synthesis. The following three main approaches are considered as a primary classification of the methods: modelling of the target – ligand coordination without structural information on both the target and the resulting complex; calculations based on experimentally obtained structural information about the target; and dynamic simulation of the target – ligand complex and the reaction mechanism with calculation of the free energy of the reaction. The review is meant for synthetic chemists to be used as a guide for building an algorithm for preliminary modelling and synthesis of structures with specified properties.

The bibliography includes 353 references.

Computational Strategy Revealing the Structural Determinant of Ligand Selectivity towards Highly Similar Protein Targets

BACKGROUND

Poor selectivity of drug candidates may lead to toxicity and side effects accounting for as high as 60% failure rate, thus, the selectivity is consistently significant and challenging for drug discovery.

OBJECTIVE

To find highly specific small molecules towards very similar protein targets, multiple strategies are always employed, including (1) To make use of the diverse shape of binding pocket to avoid steric bump; (2) To increase binding affinities for favorite residues; (3) To achieve selectivity through allosteric regulation of target; (4) To stabalize the inactive conformation of protein target and (5) To occupy dual binding pockets of single target.

CONCLUSION

In this review, we summarize computational strategies along with examples of their successful applications in designing selective ligands, with the aim to provide insights into everdiversifying drug development practice and inspire medicinal chemists to utilize computational strategies to avoid potential side effects due to low selectivity of ligands.

Practices in Molecular Docking and Structure-Based Virtual Screening

Drug discovery has evolved significantly over the past two decades. Progress in key areas such as molecular and structural biology has contributed to the elucidation of the three-dimensional structure and function of a wide range of biological molecules of therapeutic interest. In this context, the integration of experimental techniques, such as X-ray crystallography, and computational methods, such as molecular docking, has promoted the emergence of several areas in drug discovery, such as structure-based drug design (SBDD). SBDD strategies have been broadly used to identify, predict and optimize the activity of small molecules toward a molecular target and have contributed to major scientific breakthroughs in pharmaceutical R&D. This chapter outlines molecular docking and structure-based virtual screening (SBVS) protocols used to predict the interaction of small molecules with the phosphatidylinositol-bisphosphate-kinase PI3Kδ, which is a molecular target for hematological diseases. A detailed description of the molecular docking and SBVS procedures and an evaluation of the results are provided.

Assessment of antidotal efficacy of cholinesterase reactivators against paraoxon: In vitro reactivation kinetics and physicochemical properties

The search of proficient oximes as reactivators of irreversibly inhibited-AChE by organophosphate poisoning necessitates an appropriate assessment of their physicochemical properties and reactivation kinetics. Therefore, herein acid dissociation constant; pKa, lipophilicity; logP, polar surface area, hydrogen bond donor and acceptor counts of structurally different oximes (two tertiary oximes and thirteen pyridinium aldoxime derivatives) have been evaluated. The experimentally obtained data for pKa has been comparatively analyzed by using non-linear regression. Further the tested oximes were screened through in vitro reactivation kinetics against paraoxon-inhibited AChE. The pKa values of all the examined oximes were within the range of 7.50-9.53. pKa values of uncharged and mono-pyridinium oximes were in good correlation with their reactivation potency. The high negative logP values of pyridinium oxime reactivators indicate their high hydrophilic character; hence oximes with improved lipophilicity should be designed for the development of novel and more potent antidotes. Propane and butane linked oximes were superior reactivators than xylene linked bis-oxime reactivators. It is concluded from the present study that pKa value is not only ruled by the position of oximino functionality in the pyridinium ring, but also by the position of linker. Although, pyridinium oximes are proved to be better reactivators but their lipophilicity has to be improved.

Computational analysis of structure-based interactions and ligand properties can predict efflux effects on antibiotics

AcrA-AcrB-TolC efflux pumps extrude drugs of multiple classes from bacterial cells and are a leading cause for antimicrobial resistance. Thus, they are of paramount interest to those engaged in antibiotic discovery. Accurate prediction of antibiotic efflux has been elusive, despite several studies aimed at this purpose. Minimum inhibitory concentration (MIC) ratios of 32 β-lactam antibiotics were collected from literature. 3-Dimensional Quantitative Structure-Activity Relationship on the β-lactam antibiotic structures revealed seemingly predictive models (q(2)=0.53), but the lack of a general superposition rule does not allow its use on antibiotics that lack the β-lactam moiety. Since MIC ratios must depend on interactions of antibiotics with lipid membranes and transport proteins during influx, capture and extrusion of antibiotics from the bacterial cell, descriptors representing these factors were calculated and used in building mathematical models that quantitatively classify antibiotics as having high/low efflux (>93% accuracy). Our models provide preliminary evidence that it is possible to predict the effects of antibiotic efflux if the passage of antibiotics into, and out of, bacterial cells is taken into account--something descriptor and field-based QSAR models cannot do. While the paucity of data in the public domain remains the limiting factor in such studies, these models show significant improvements in predictions over simple LogP-based regression models and should pave the path toward further work in this field. This method should also be extensible to other pharmacologically and biologically relevant transport proteins.

From chemical documentation to chemoinformatics: 50 years of chemical information science

This paper summarizes the historical development of the discipline that is now called `chemoinformatics'. It shows how this has evolved, principally as a result of technological developments in chemistry and biology during the past decade, from long-established techniques for the modelling and searching of chemical molecules. A total of 30 papers, the earliest dating back to 1957, are briefly summarized to highlight some of the key publications and to show the development of the discipline.

Combining docking and molecular dynamic simulations in drug design

A rational approach is needed to maximize the chances of finding new drugs, and to exploit the opportunities of potential new drug targets emerging from genomic and proteomic initiatives, and from the large libraries of small compounds now readily available through combinatorial chemistry. Despite a shaky early history, computer-aided drug design techniques can now be effective in reducing costs and speeding up drug discovery. This happy outcome results from development of more accurate and reliable algorithms, use of more thoughtfully planned strategies to apply them, and greatly increased computer power to allow studies with the necessary reliability to be performed. Our review focuses on applications and protocols, with the main emphasis on critical analysis of recent studies where docking calculations and molecular dynamics (MD) simulations were combined to dock small molecules into protein receptors. We highlight successes to demonstrate what is possible now, but also point out drawbacks and future directions. The review is structured to lead the reader from the simpler to more compute-intensive methods. Thus, while inexpensive and fast docking algorithms can be used to scan large compound libraries and reduce their size, more accurate but expensive MD simulations can be applied when a few selected ligand candidates remain. MD simulations can be used: during the preparation of the protein receptor before docking, to optimize its structure and account for protein flexibility; for the refinement of docked complexes, to include solvent effects and account for induced fit; to calculate binding free energies, to provide an accurate ranking of the potential ligands; and in the latest developments, during the docking process itself to find the binding site and correctly dock the ligand a priori.

In Silico Predictions of Blood-Brain Barrier Penetration: Considerations to “Keep in Mind”

Within drug discovery, it is desirable to determine whether a compound will penetrate and distribute within the central nervous system (CNS) with the requisite pharmacokinetic and pharmacodynamic performance required for a CNS target or if it will be excluded from the CNS, wherein potential toxicities would mitigate its applicability. A variety of in vivo and in vitro methods for assessing CNS penetration have therefore been developed and applied to advancing drug candidates with the desired properties. In silico methods to predict CNS penetration from chemical structures have been developed to address virtual screening and prospective design. In silico predictive methods are impacted by the quality, quantity, sources, and generation of the measured data available for model development. Key considerations for predictions of CNS penetration include the comparison of local (in chemistry space) versus global (more structurally diverse) models and where in the drug discovery process such models may be best deployed. Preference should also be given to in vitro and in vivo measurements of greater mechanistic clarity that better support the development of structure-property relationships. Although there are numerous statistical methods that have been brought to bear on the prediction of CNS penetration, a greater concern is that such models are appropriate for the quality of measured data available and are statistically validated. In addition, the assessment of prediction uncertainty and relevance of predictive models to structures of interest are critical. This article will address these key considerations for the development and application of in silico methods in drug discovery.