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.

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Computational Approaches in Preclinical Studies on Drug Discovery and Development.

Worldwide, Alzheimer's disease (AD) is the most common neurodegenerative multifactorial disease influencing the elderly population. Nowadays, several medications, among them curcumin, are used in the treatment of AD. Curcumin, which is the principal component of Curcuma longa, has shown favorable effects forsignificantly preventing or treating AD. During the last decade, the scientific community has focused their research on the optimization of therapeutic properties and on the improvement of pharmacokinetic properties of curcumin. This review summarizes bibliographical data from 2009 to 2019 on curcumin analogues, derivatives, and hybrids, as well as their therapeutic, preventic, and diagnostic applications in AD. Recent advances in the field have revealed that the phenolic hydroxyl group could contribute to the anti-amyloidogenic activity. Phenyl methoxy groups seem to contribute to the suppression of amyloid-β peptide (Aβ42) and to the suppression of amyloid precursor protein (APP) andhydrophobic interactions have also revealed a growing role. Furthermore, flexible moieties, at the linker, are crucial for the inhibition of Aβ aggregation. The inhibitory activity of derivatives is increased with the expansion of the aromatic rings. The promising role of curcumin-based compounds in diagnostic imaging is highlighted. The keto-enol tautomerism seems to be a novel modification for the design of amyloid-binding agents. Molecular docking results, (Q)SAR, as well as in vitro and in vivo tests highlight the structures and chemical moieties that are correlated with specific activity. As a result, the knowledge gained from the existing research should lead to the design and synthesis ofinnovative and multitargetedcurcumin analogues, derivatives, or curcumin hybrids, which would be very useful drug and tools in medicine for both diagnosis and treatment of AD.

Curcumin in Health and Diseases: Alzheimer's Disease and Curcumin Analogues, Derivatives, and Hybrids.

Computational methodologies are assisting the exploration of marine natural products (MNPs) to make the discovery of new leads more efficient, to repurpose known MNPs, to target new metabolites on the basis of genome analysis, to reveal mechanisms of action, and to optimize leads. In silico efforts in drug discovery of NPs have mainly focused on two tasks: dereplication and prediction of bioactivities. The exploration of new chemical spaces and the application of predicted spectral data must be included in new approaches to select species, extracts, and growth conditions with maximum probabilities of medicinal chemistry novelty. In this review, the most relevant current computational dereplication methodologies are highlighted. Structure-based (SB) and ligand-based (LB) chemoinformatics approaches have become essential tools for the virtual screening of NPs either in small datasets of isolated compounds or in large-scale databases. The most common LB techniques include Quantitative Structure⁻Activity Relationships (QSAR), estimation of drug likeness, prediction of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, similarity searching, and pharmacophore identification. Analogously, molecular dynamics, docking and binding cavity analysis have been used in SB approaches. Their significance and achievements are the main focus of this review.

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Computational Methodologies in the Exploration of Marine Natural Product Leads.

Tuberculosis is a ravaging disease killing one person in every 1.5 minutes in India alone. Someone in the world is newly infected with TB bacilli every second. Overall, one-third of the world's population is currently infected with the TB bacillus. Left untreated, each person with active TB disease will infect on average between 10 and 15 people every year. 450 000 new MDR-TB cases are estimated to occur every year. Estimated 5% of TB patients are HIV infected. Although the complete genome sequence of the causative pathogen _Mycobacterium tuberculosis_ was published about a decade ago and many years of painstaking efforts have been invested, we are still far from having a good, fast acting drug and vaccine which confers long lasting protection. Despite increasing investment, led by charities including the Gates Foundation, no novel drugs for TB have entered the market for many years.Novel mechanisms for attracting funding, particularly private sector funding, into this research area are desperately needed. We wish to bring in the power of genomics, computational technologies and participation of young and brilliant talent from Universities and Industrial partners with a strong inclination to apply a concerted effort to address this important scourge. All researchers contribute data on tuberculosis drug targets and active molecules through a copyleft agreement; anyone who is prepared to keep to this may participate. All the data is Clickwrap protected and credit sharing will be based on a novel and flexible micro-attribution system. This system is aimed at providing due credit through an active process. Various levels of investigators shall have appropriate levels of rights, recognition and responsibilities. Another valuable aspect is the partnerships of Industry with belief in Open Source systems and models. We name this concerted effort to tackle this dreaded disease as Open Source Drug Discovery for Tuberculosis.

Open Source Drug Discovery

Aging-associated neurodegenerative diseases, which are characterized by progressive neuronal death and synapses loss in human brain, are rapidly growing affecting millions of people globally. Alzheimer's is the most common neurodegenerative disease and it can be caused by genetic and environmental risk factors. This review describes the amyloid-β and Tau hypotheses leading to amyloid plaques and neurofibrillary tangles, respectively which are the predominant pathways for the development of anti-Alzheimer's small molecule inhibitors. The function and structure of the druggable targets of these two pathways including β-secretase, γ-secretase, and Tau are discussed in this review article. Computer-Aided Drug Design including computational structure-based design and ligand-based design have been employed successfully to develop inhibitors for biomolecular targets involved in Alzheimer's. The application of computational molecular modeling for the discovery of small molecule inhibitors and modulators for β-secretase and γ-secretase is summarized. Examples of computational approaches employed for the development of anti-amyloid aggregation and anti-Tau phosphorylation, proteolysis and aggregation inhibitors are also reported.

https://www.mdpi.com/1422-0067/21/3/703/pdf

Computer-Aided Drug Design of β-Secretase, γ-Secretase and Anti-Tau Inhibitors for the Discovery of Novel Alzheimer's Therapeutics.

Molecular docking and QSAR analyses for understanding the antimalarial activity of some 7-substituted-4-aminoquinoline derivatives.

The inhibition of abnormal amyloid β (Aβ) aggregation has been regarded as a good target to control Alzheimer’s disease. The present study adopted 2D-QSAR, HQSAR and 3D QSAR (CoMFA & CoMSIA) modeling approaches to identify the structural and physicochemical requirements for the potential Aβ aggregation inhibition. A structure-based molecular docking technique is utilized to approve the features that are obtained from the ligand-based techniques on 30 curcumin derivatives. The combined outputs were then used to screen the modified 10 compounds. The 2D QSAR model on curcumin derivatives gave statistical values R2 = 0.9086 and SEE = 0.1837. The model was further confirmed by Y-randomization test and Applicability domain analysis by the standardization approach. The HQSAR study (Q2 = 0.615, R
 ncv
 2
  = 0.931, R
 pred
 2
  = 0.956) illustrated the important molecular fingerprints for inhibition. Contour maps of 3D QSAR models, CoMFA (Q2 = 0.687, R
 ncv
 2
  = 0.787, R
 pred
 2
  = 0.731) and CoMSIA (Q2 = 0.743, R
 ncv
 2
  = 0.972, R
 pred
 2
  = 0.713), depict that the models are robust and provide explanation of the important features, like steric, electrostatic and hydrogen bond acceptor, which play important role for interaction with the receptor site cavity. The molecular docking study of the curcumin derivatives elucidates the important interactions between the amino acid residues at the catalytic site of the receptor and the ligands, indicating the structural requirements of the inhibitors. The ligand–receptor interactions of top hits were analyzed to explore the pharmacophore features of Aβ aggregation inhibition. The Aβ aggregation inhibitory activities of novel chemical entities were then obtained through inverse QSAR. The newly designed molecules were further screened through machine learning, prediction of toxicity and nature of metabolism to get the proposed six lead compounds.

/pdf/design-of-novel-amyloid-b-aggregation-inhibitors-using-qsar-18ot5l09o9.pdf

Design of novel amyloid β aggregation inhibitors using QSAR, pharmacophore modeling, molecular docking and ADME prediction

In this work, an attempt was made to propose new leads based on the natural scaffold Thiaplakortone-A active against malaria. The 2D QSAR studies suggested that three descriptors correlate with the anti-malarial activity with an R2 value of 0.814. Robustness, reliability, and predictive power of the model were tested by internal validation, external validation, Y-scrambling, and applicability domain analysis. HQSAR studies were carried out as an additional tool to find the sub-structural fingerprints. The CoMFA and CoMSIA models gave Q2 values of 0.813 and 0.647, and [Formula: see text] values of 0.994 and 0.984, respectively. Using the 2D-QSAR equation, the activity values of the seven modified compounds were calculated and it was found that three molecules showed good anti-malarial activity. Molecular docking of the 42 Thiaplakortone-A derivatives with Plasmodium falciparum calcium-dependent protein kinase 1 (PfCDPK1) was carried out to find out protein-ligand interactions. Data mining of the bioassay data-set AID: 504850 using the classifier based on Random Forest of Weka suggested that all of the eight molecules selected and three out of the seven virtual molecules were anti-malarial active. Both the virtual molecules and drug molecules were docked with CYP3A4, indicating that the virtual molecules could metabolize easily. Toxicity studies using Osiris shows that three molecules showed no toxic characters.

Computational strategies to explore antimalarial thiazine alkaloid lead compounds based on an Australian marine sponge Plakortis Lita.

Malaria parasites show resistance to most of the antimalarial drugs and hence developing antimalarials which can act on multitargets rather than a single target will be a promising strategy of drug design. Here we report a new approach by which virtual screening of 292 unique phytochemicals present in 72 traditionally important herbs is used for finding out inhibitors of plasmepsin-2 and falcipain-2 for antimalarial activity against P. falciparum. Initial screenings of the selected molecules by Random Forest algorithm model of Weka using the bioassay datasets AID 504850 and AID 2302 screened 120 out of the total 292 phytochemicals to be active against the targets. Toxtree scan cautioned 21 compounds to be either carcinogenic or mutagenic and were thus removed for further analysis. Out of the remaining 99 compounds, only 46 compounds offered drug-likeness as per the 'rule of five' criteria. Out of ten antimalarial drug targets, only two target proteins such as 3BPF and 3PNR of falcipain-2 and 1PFZ and 2BJU of plasmepsin-2 are selected as targets. The potential binding of the selected 46 compounds to the active sites of these four targets was analyzed using MOE software. The docked conformations and the interactions with the binding pocket residues of the target proteins were understood by 'Ligplot' analysis. It has been found that 8 compounds are dual inhibitors of falcipain-2 and plasmepsin-2, with the best binding energies. Compound 117 (6aR, 12aS)-12a-Hydroxy-9-methoxy-2,3-dimethylenedioxy-8-prenylrotenone (Usaratenoid C) present in the plant Millettia usaramensis showed maximum molecular docking score.

Virtual Screening Techniques to Probe the Antimalarial Activity of some Traditionally Used Phytochemicals.

This study focuses on understanding the structural features of 3,6-dihydroimidazo(4,5-d)pyrrolo(2,3-b)pyridin-2(1H)-one (dpp) derivatives to computationally identify new JAK inhibiting compounds. For the purpose, a novel virtual screening strategy, with 2D and 3D-QSAR (CoMFA and CoMSIA), data mining, pharmacophore modeling, ADMET prediction, multi-targeted protein-based docking and inverse QSAR, was employed. The 2D-QSAR equations developed for the JAK3, JAK2 and JAK1 involved five physicochemical descriptors. These descriptors correlate with the anti-RA activity with R2 values for JAK3, JAK2 and JAK1 are 0.9811, 0.8620 and 0.9740, respectively. The 3D-QSAR studies such as CoMFA and CoMSIA carried out through PLS analysis of the training set of JAK3, JAK2 and JAK1, gave Q2 values as 0.369, 0.476 and 0.490; [Formula: see text] values as 0.863, 0.684 and 0.724 and, F values as 23.098, 28.139 and 31.438, respectively. The contour maps produced by the CoMFA and CoMSIA models were used to understand the importance of hydrogen bond donor, acceptor, hydrophobic, steric and electrostatic interactions. The molecular docking studies of these selected compounds with various JAK proteins were carried out and the protein-ligand interactions were also studied. The study concluded that dpp15(s) is a highly potent JAK inhibitor with a very good predicted IC50 value.

/pdf/exploration-of-3-6-dihydroimidazo-4-5-d-pyrrolo-2-3-b-ftsjk8rydp.pdf

Lilly Aswathy

Papers

Molecular docking and QSAR analyses for understanding the antimalarial activity of some 7-substituted-4-aminoquinoline derivatives.

Design of novel amyloid β aggregation inhibitors using QSAR, pharmacophore modeling, molecular docking and ADME prediction

Computational strategies to explore antimalarial thiazine alkaloid lead compounds based on an Australian marine sponge Plakortis Lita.

Virtual Screening Techniques to Probe the Antimalarial Activity of some Traditionally Used Phytochemicals.

Exploration of 3,6-dihydroimidazo(4,5-d)pyrrolo(2,3-b)pyridin-2(1H)-one derivatives as JAK inhibitors using various in silico techniques