Anwar Rayan

Understanding drug‐likeness

‘Drug‐likeness’, a qualitative property of chemicals assigned by experts committee vote, is widely integrated into the early stages of lead and drug discovery. Its conceptual evolution paralleled work related to Pfizers ‘rule of five’ and lead‐likeness, and is placed within this framework. The discrimination between ‘drugs’ (represented by a collection of pharmaceutically relevant small molecules, some of which are marketed drugs) and ‘nondrugs’ (typically, chemical reagents) is possible using a wide variety of statistical tools and chemical descriptor systems. Here we summarize 18 papers focused on drug‐likeness, and provide a comprehensive overview of progress in the field. Tools that estimate drug‐likeness are valuable in the early stages of lead discovery, and can be used to filter out compounds with undesirable properties from screening libraries and to prioritize hits from primary screens. As the goal is, most often, to develop orally available drugs, it is also useful to optimize drug‐like pharmacokinetic properties. We examine tools that evaluate drug‐likeness and some of their shortcomings, challenges facing these tools, and address the following issues: What is the definition of drug‐likeness and how can it be utilized to reduce attrition rate in drug discovery? How difficult is it to distinguish drugs from nondrugs? Are nondrug datasets reliable? Can we estimate oral drug‐likeness? We discuss a drug‐like filter and recent advances in the prediction of oral drug‐likeness. The heuristic aspect of drug‐likeness is also addressed.

Blood–brain barrier permeability and tPA-mediated neurotoxicity

Tissue type plasminogen activator (tPA) can induce neuronal apoptosis, disrupt the blood-brain barrier (BBB), and promote dilation of the cerebral vasculature. The timing, sequence and contributions of these and other deleterious effects of tPA and their contribution to post-ischemic brain damage after stroke, have not been fully elucidated. To dissociate the effects of tPA on BBB permeability, cerebral vasodilation and protease-dependent pathways, we developed several tPA mutants and PAI-1 derived peptides constructed by computerized homology modeling of tPA. Our data show that intravenous administration of human tPA to rats increases BBB permeability through a non-catalytic process that is associated with reversible neurotoxicity, brain damage, mortality and contributes significantly to its brief therapeutic window. Furthermore, our data show that inhibiting the effect of tPA on BBB function without affecting its catalytic activity, improves outcome and significantly extends its therapeutic window in mechanical as well as in thromboembolic models of stroke.

A stochastic algorithm for global optimization and for best populations: A test case of side chains in proteins

The problem of global optimization is pivotal in a variety of scientific fields. Here, we present a robust stochastic search method that is able to find the global minimum for a given cost function, as well as, in most cases, any number of best solutions for very large combinatorial “explosive” systems. The algorithm iteratively eliminates variable values that contribute consistently to the highest end of a cost functions spectrum of values for the full system. Values that have not been eliminated are retained for a full, exhaustive search, allowing the creation of an ordered population of best solutions, which includes the global minimum. We demonstrate the ability of the algorithm to explore the conformational space of side chains in eight proteins, with 54 to 263 residues, to reproduce a population of their low energy conformations. The 1,000 lowest energy solutions are identical in the stochastic (with two different seed numbers) and full, exhaustive searches for six of eight proteins. The others retain the lowest 141 and 213 (of 1,000) conformations, depending on the seed number, and the maximal difference between stochastic and exhaustive is only about 0.15 Kcal/mol. The energy gap between the lowest and highest of the 1,000 low-energy conformers in eight proteins is between 0.55 and 3.64 Kcal/mol. This algorithm offers real opportunities for solving problems of high complexity in structural biology and in other fields of science and technology.

Cancer Treatment by Greco-Arab and Islamic Herbal Medicine

Islamic medicine, Arabic medicine, Arab-Islamic medicine, or Greco-Arab and Islamic medicine refers to medicine developed in the Golden Age of the Arab-Islamic civilization, which extended from Spain in the west to Central Asia and India in the east. In temporal terms it covered a period of roughly nine centuries, from the middle of the seventh to the end of the fifteenth century. Medicine was a central part of this medieval civilization. Famous Arab and Muslim physicians, e.g., Rhazes, Avicenna, Al Zahrawi, Ibn al Nafis studied and developed treatments regimes for cancer as well as most known diseases at that time. They described most types of cancers which were known at that time and suggested several therapies. This review is an eye-bird view on the ancient Arab-Greco and Islamic cancer diagnosis, herbal treatment and nowadays herbal treatment research.

New vistas in GPCR 3D structure prediction

Human G-protein coupled receptors (hGPCRs) comprise the most prominent family of validated drug targets. More than 50% of approved drugs reveal their therapeutic effects by targeting this family. Accurate models would greatly facilitate the process of drug discovery and development. However, 3-D structure prediction of GPCRs remains a challenge due to limited availability of resolved structure. The X-ray structures have been solved for only four such proteins. The identity between hGPCRs and the potential templates is mostly less than 30%, well below the level at which sequence alignment can be done regularly. In this study, we analyze a large database of human G-protein coupled receptors that are members of family A in order to optimize usage of the available crystal structures for molecular modeling of hGPCRs. On the basis of our findings in this study, we propose to regard specific parts from the trans-membrane domains of the reference receptor helices as appropriate template for constructing models of other GPCRs, while other residues require other techniques for their remodeling and refinement. The proposed hypothesis in the current study has been tested by modeling human β2-adrenergic receptor based on crystal structures of bovine rhodopsin (1F88) and human A2A adenosine receptor (3EML). The results have shown some improvement in the quality of the predicted models compared to Modeller software.

Anticancer Activity of Anise (Pimpinella anisum L.) Seed Extract

Cancer incidence is much lower in India than in western countries. The reason is not fully understood, but the high spice consumption could be one of the contributing factors. Anise is one of the plants grows in India and people from our region believe that anise seeds are helpful in cancer prevention and treatment. In this study, anticancer activity of eth- anol extract of anise (Pimpinella anisum L.) seed was investigated. MTT and LDH assays revealed that ethanolic extract have cytotoxic activity on human prostate cancer cell line (PC-3) at concentrations found safe to normal cells (rat skeletal muscle cell line (L6)). Treatment with anise seeds extract caused anti proliferative and apoptotic effects, with IC50 value of 400 µg/mL to cancer cells. Thus, anise could be one of the foods that attribute to cancer prevention and treatment. It could also be a natural source of novel anticancer compounds with anti proliferative and/or apoptotic properties and it is worth to work on for isolation and identification of novel anticancer drug candidates.

Indexing molecules for their hERG liability

The human Ether-a-go-go-Related-Gene (hERG) potassium (K(+)) channel is liable to drug-inducing blockage that prolongs the QT interval of the cardiac action potential, triggers arrhythmia and possibly causes sudden cardiac death. Early prediction of drug liability to hERG K(+) channel is therefore highly important and preferably obligatory at earlier stages of any drug discovery process. In vitro assessment of drug binding affinity to hERG K(+) channel involves substantial expenses, time, and labor; and therefore computational models for predicting liabilities of drug candidates for hERG toxicity is of much importance. In the present study, we apply the Iterative Stochastic Elimination (ISE) algorithm to construct a large number of rule-based models (filters) and exploit their combination for developing the concept of hERG Toxicity Index (ETI). ETI estimates the molecular risk to be a blocker of hERG potassium channel. The area under the curve (AUC) of the attained model is 0.94. The averaged ETI of hERG binders, drugs from CMC, clinical-MDDR, endogenous molecules, ACD and ZINC, were found to be 9.17, 2.53, 3.3, -1.98, -2.49 and -3.86 respectively. Applying the proposed hERG Toxicity Index Model on external test set composed of more than 1300 hERG blockers picked from chEMBL shows excellent performance (Matthews Correlation Coefficient of 0.89). The proposed strategy could be implemented for the evaluation of chemicals in the hit/lead optimization stages of the drug discovery process, improve the selection of drug candidates as well as the development of safe pharmaceutical products.

Physicochemical Properties of Natural Based Products versus Synthetic Chemicals

The majority of the currently used cosmetics and drugs are natural products-based compounds or their deriva- tives. This could add weight to the argument that natural based products are inherently better tolerated in the body than synthetic chemicals and have higher chance to be approved as new drugs. The present study was undertaken to analyze a natural product database compared to synthetic chemicals and to search for discriminative physicochemical properties that may probably help in differentiating between natural and synthetic compounds. We have formulated rules to assess the natural likeness of chemicals and thereby discriminate between natural-based and synthetic chemicals. A Mathews Corre- lation Coefficient of 0.5 was obtained; nearly 81% of natural-based products and 68% of synthetic chemicals were pre- cisely classified using this filter. The property criteria for drug-likeness and lead-likeness are more pronounced in natural products rather than synthetic ones. The fraction of synthetic chemicals which are natural-like could have higher chance to be successful drug.

Nature is the best source of anticancer drugs: Indexing natural products for their anticancer bioactivity

Cancer is considered one of the primary diseases that cause morbidity and mortality in millions of people worldwide and due to its prevalence, there is undoubtedly an unmet need to discover novel anticancer drugs. However, the traditional process of drug discovery and development is lengthy and expensive, so the application of in silico techniques and optimization algorithms in drug discovery projects can provide a solution, saving time and costs. A set of 617 approved anticancer drugs, constituting the active domain, and a set of 2,892 natural products, constituting the inactive domain, were employed to build predictive models and to index natural products for their anticancer bioactivity. Using the iterative stochastic elimination optimization technique, we obtained a highly discriminative and robust model, with an area under the curve of 0.95. Twelve natural products that scored highly as potential anticancer drug candidates are disclosed. Searching the scientific literature revealed that few of those molecules (Neoechinulin, Colchicine, and Piperolactam) have already been experimentally screened for their anticancer activity and found active. The other phytochemicals await evaluation for their anticancerous activity in wet lab.

How to Choose the Suitable Template for Homology Modelling of GPCRs: 5-HT7 Receptor as a Test Case

G protein‐coupled receptors (GPCRs) are a super‐family of membrane proteins that attract great pharmaceutical interest due to their involvement in almost every physiological activity, including extracellular stimuli, neurotransmission, and hormone regulation. Currently, structural information on many GPCRs is mainly obtained by the techniques of computer modelling in general and by homology modelling in particular. Based on a quantitative analysis of eighteen antagonist‐bound, resolved structures of rhodopsin family “A” receptors – also used as templates to build 153 homology models – it was concluded that a higher sequence identity between two receptors does not guarantee a lower RMSD between their structures, especially when their pair‐wise sequence identity (within trans‐membrane domain and/or in binding pocket) lies between 25 % and 40 %. This study suggests that we should consider all template receptors having a sequence identity ≤50 % with the query receptor. In fact, most of the GPCRs, compared to the currently available resolved structures of GPCRs, fall within this range and lack a correlation between structure and sequence. When testing suitability for structure‐based drug design, it was found that choosing as a template the most similar resolved protein, based on sequence resemblance only, led to unsound results in many cases. Molecular docking analyses were carried out, and enrichment factors as well as attrition rates were utilized as criteria for assessing suitability for structure‐based drug design.