Catalyzing a Cure: Discovery and development of LRRK2 inhibitors for the treatment of Parkinson's disease.

Parkinson's disease (PD) is an age-related second most common progressive neurodegenerative disorder that affects millions of people worldwide. Despite decades of research, no effective disease modifying therapeutics have reached clinics for treatment/management of PD. Leucine-rich repeat kinase 2 (LRRK2) which controls membrane trafficking and lysosomal function and its variant LRRK2-G2019S are involved in the development of both familial and sporadic PD. LRRK2, is therefore considered as a legitimate target for the development of therapeutics against PD. During the last decade, efforts have been made to develop effective, safe and selective LRRK2 inhibitors and also our understanding about LRRK2 has progressed. However, there is an urge to learn from the previously designed and reported LRRK2 inhibitors in order to effectively approach designing of new LRRK2 inhibitors. In this review, we have aimed to cover the pre-clinical studies undertaken to develop small molecule LRRK2 inhibitors by screening the patents and other available literature in the last decade. We have highlighted LRRK2 as targets in the progress of PD and subsequently covered detailed design, synthesis and development of diverse scaffolds as LRRK2 inhibitors. Moreover, LRRK2 inhibitors under clinical development has also been discussed. LRRK2 inhibitors seem to be potential targets for future therapeutic interventions in the treatment and management of PD and this review can act as a cynosure for guiding discovery, design, and development of selective and non-toxic LRRK2 inhibitors. Although, there might be challenges in developing effective LRRK2 inhibitors, the opportunity to successfully develop novel therapeutics targeting LRRK2 against PD has never been greater.

Artificial intelligence assisted identification of potential tau aggregation inhibitors: ligand- and structure-based virtual screening, in silico ADME, and molecular dynamics study.

Alzheimer's disease (AD) is a severe, growing, multifactorial disorder affecting millions of people worldwide characterized by cognitive decline and neurodegeneration. The accumulation of tau protein into paired helical filaments is one of the major pathological hallmarks of AD and has gained the interest of researchers as a potential drug target to treat AD. Lately, Artificial Intelligence (AI) has revolutionized the drug discovery process by speeding it up and reducing the overall cost. As a part of our continuous effort to identify potential tau aggregation inhibitors, and leveraging the power of AI, in this study, we used a fully automated AI-assisted ligand-based virtual screening tool, PyRMD to screen a library of 12 million compounds from the ZINC database to identify potential tau aggregation inhibitors. The preliminary hits from virtual screening were filtered for similar compounds and pan-assay interference compounds (the compounds containing reactive functional groups which can interfere with the assays) using RDKit. Further, the selected compounds were prioritized based on their molecular docking score with the binding pocket of tau where the binding pockets were identified using replica exchange molecular dynamics simulation. Thirty-three compounds showing good docking scores for all the tau clusters were selected and were further subjected to in silico pharmacokinetic prediction. Finally, top 10 compounds were selected for molecular dynamics simulation and MMPBSA binding free energy calculations resulting in the identification of UNK_175, UNK_1027, UNK_1172, UNK_1173, UNK_1237, UNK_1518, and UNK_2181 as potential tau aggregation inhibitors.

N-glycosylation induced changes in tau protein dynamics reveal its role in tau misfolding and aggregation: A microsecond long molecular dynamics study.

Various posttranslational modifications like hyperphosphorylation, O-GlcNAcylation, and acetylation have been attributed to induce the abnormal folding in tau protein. Recent in vitro studies revealed the possible involvement of N-glycosylation of tau protein in the abnormal folding and tau aggregation. Hence, in this study, we performed a microsecond long all atom molecular dynamics simulation to gain insights into the effects of N-glycosylation on Asn-359 residue which forms part of the microtubule binding region. Trajectory analysis of the stimulations coupled with essential dynamics and free energy landscape analysis suggested that tau, in its N-glycosylated form tends to exist in a largely folded conformation having high beta sheet propensity as compared to unmodified tau which exists in a large extended form with very less beta sheet propensity. Residue interaction network analysis of the lowest energy conformations further revealed that Phe378 and Lys353 are the functionally important residues in the peptide which helped in initiating the folding process and Phe378, Lys347, and Lys370 helped to maintain the stability of the protein in the folded state.

Exploration of Novel Lichen Compounds as Inhibitors of SARS-CoV-2 Mpro: Ligand-Based Design, Molecular Dynamics, and ADMET Analyses.

In the year 2019-2020, the whole world witnessed the spread of a disease called COVID-19 caused by SARS-CoV-2. A number of effective drugs and vaccine has been formulated to combat this outbreak. For the development of anti-COVID-19 drugs, the main protease (Mpro) is considered a key target as it has rare mutations and plays a crucial role in the replication of the SARS CoV-2. In this study, a library of selected lichen compounds was prepared and used for virtual screening against SARS-CoV-2 Mpro using molecular docking, and several hits as potential inhibitors were identified. Remdesivir was used as a standard inhibitor of Mpro for its comparison with the identified hits. Twenty-six compounds were identified as potential hits against Mpro, and these were subjected to in silico ADMET property prediction, and the compounds having favorable properties were selected for further analysis. After manual inspection of their interaction with the binding pocket of Mpro and binding affinity score, four compounds, namely, variolaric acid, cryptostictinolide, gyrophoric acid, and usnic acid, were selected for molecular dynamics study to evaluate the stability of complex. The molecular dynamics results indicated that except cryptostictinolide, all the three compounds made a stable complex with Mpro throughout a 100-ns simulation time period. Among all, usnic acid seems to be more stable and effective against SARS-CoV-2 Mpro. In summary, our findings suggest that usnic acid, variolaric acid, and gyrophoric acid have potential to inhibit SARS-Cov-2 Mpro and act as a lead compounds for the development of antiviral drug candidates against SARS-CoV-2.

Deep learning tools for advancing drug discovery and development.

A few decades ago, drug discovery and development were limited to a bunch of medicinal chemists working in a lab with enormous amount of testing, validations, and synthetic procedures, all contributing to considerable investments in time and wealth to get one drug out into the clinics. The advancements in computational techniques combined with a boom in multi-omics data led to the development of various bioinformatics/pharmacoinformatics/cheminformatics tools that have helped speed up the drug development process. But with the advent of artificial intelligence (AI), machine learning (ML) and deep learning (DL), the conventional drug discovery process has been further rationalized. Extensive biological data in the form of big data present in various databases across the globe acts as the raw materials for the ML/DL-based approaches and helps in accurate identifications of patterns and models which can be used to identify therapeutically active molecules with much fewer investments on time, workforce and wealth. In this review, we have begun by introducing the general concepts in the drug discovery pipeline, followed by an outline of the fields in the drug discovery process where ML/DL can be utilized. We have also introduced ML and DL along with their applications, various learning methods, and training models used to develop the ML/DL-based algorithms. Furthermore, we have summarized various DL-based tools existing in the public domain with their application in the drug discovery paradigm which includes DL tools for identification of drug targets and drug-target interaction such as DeepCPI, DeepDTA, WideDTA, PADME DeepAffinity, and DeepPocket. Additionally, we have discussed various DL-based models used in protein structure prediction, de novo design of new chemical scaffolds, virtual screening of chemical libraries for hit identification, absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction, metabolite prediction, clinical trial design, and oral bioavailability prediction. In the end, we have tried to shed light on some of the successful ML/DL-based models used in the drug discovery and development pipeline while also discussing the current challenges and prospects of the application of DL tools in drug discovery and development. We believe that this review will be useful for medicinal and computational chemists searching for DL tools for use in their drug discovery projects.

Structural modification aimed for improving solubility of lead compounds in early phase drug discovery.

Many lead compounds fail to reach clinical trials despite being potent because of low bioavailability attributed to their insufficient solubility making solubility a primary and crucial factor in early phase drug discovery. Solubility improvement of poorly soluble lead compounds without losing potency is a challenging task for the medicinal chemist in a drug discovery setup. Solubility is an important factor not only to dissipate or liquefy a substance but also to attain an optimal concentration of drug in systemic circulation required for the desired therapeutic effect. It has been estimated that more than forty percent of newly developed molecules are practically insoluble in water. Molecules with poor solubility not only cause difficulty for in vitro and in vivo assays but also add significant burdens to drug development in the form of longer time taken and increased cost to optimize the solubility. To tackle this problem, different techniques are being used such as physical, chemical, and miscellaneous methods to enhance solubility. Among them, the medicinal chemistry approach focussed on structural modification is a versatile and unique approach in way that it can also improve other pharmacokinetic/physicochemical parameters simultaneously. In this review, we have begun with brief introduction of solubility and its role followed by recent successful examples of different structural modification tactics reported in the literature including synthesis of prodrugs, hydrophilic and ionizable group insertion, addition & removal of hydrogen bonding, bioisosterism, disruption of molecular symmetry and planarity. Moreover, we have included a section on the obstacles in the solubility optimization and also summarised different in silico tools with potential application in solubility prediction. Overall, this review encompasses various successfully used solubility optimization examples using structure modification.