A decade of machine learning-based predictive models for human pharmacokinetics: Advances and challenges.

Traditionally, in vitro and in vivo methods are useful for estimating human pharmacokinetics (PK) parameters; however, it is impractical to perform these complex and expensive experiments on a large number of compounds. The integration of publicly available chemical, or medical Big Data and artificial intelligence (AI)-based approaches led to qualitative and quantitative prediction of human PK of a candidate drug. However, predicting drug response with these approaches is challenging, partially because of the adaptation of algorithmic and limitations related to experimental data. In this report, we provide an overview of machine learning (ML)-based quantitative structure-activity relationship (QSAR) models used in the assessment or prediction of PK values as well as databases available for obtaining such data.

Short communication for targeting natural compounds against HER2 kinase domain as potential anticancer drugs applying pharmacophore based molecular modelling approaches- part 2.

Breast cancer is one of the common causes of death noticed in women globally. In order to find effective therapeutics, the current investigation has focussed on identifying candidate compounds for EGFR and HER2. Accordingly, the pharmacophore modelling approaches were adapted to identify two prospective compounds and were docked against the target 3RCD that is complexed with TAK-285 a known dual inhibitor. Focussing on the target 3RCD, our results have showed that the compounds have demonstrated a good binding affinity towards the target occupying the binding pocket. They have established key residue interactions with stable molecular dynamics simulation results. The Hit compounds have demonstrated a potential to penetrate the blood brain barrier thereby enriching their therapeutics towards breast cancer brain metastasis. Taken together, our findings propose two candidate compounds as EGFR/HER2 inhibitors that might serve as novel chemical spaces for designing and developing new inhibitors.

Identification of novel bioactive molecules from garlic bulbs: A special effort to determine the anticancer potential against lung cancer with targeted drugs.

Garlic (Allium sativum L.), is a predominant spice, which is used as an herbal medicine and flavoring agent, since ancient times. It has a rich source of various secondary metabolites such as flavonoids, terpenoids and alkaloids, which have various pharmacological properties. Garlic is used in the treatment of various ailments such as cancer, diabetes and cardiovascular diseases. The present study aims to explore the plausible mechanisms of the selected phytocompounds as potential inhibitors against the known drug targets of non-small-cell lung cancer (NSCLC). The phytocompounds of garlic were identified by gas chromatography-mass spectrometry (GC-MS) technique. Subsequently, the identified phytocompounds were subjected to molecular docking to predict the binding with the drug targets, epidermal growth factor receptor (EGFR), human epidermal growth factor receptor 2 (HER2), echinoderm microtubule-associated protein-like 4-anaplastic lymphoma kinase (EML4-ALK) and group IIa secretory phospholipase A2 (sPLA2-IIA). Molecular dynamics is used to predict the stability of the identified phytocompounds against NSCLC drug targets by refining the intermolecular interactions formed between them. Among the 12 phytocompounds of garlic, three compounds[1,4-dimethyl-7-(1-methylethyl)-2-azulenyl]phenylmethanone, 2,4-bis(1-phenylethyl)-phenol and 4,5-2 h-oxazole-5-one,4-[3,5-di-t-butyl-4-methoxyphenyl] methylene-2-phenyl were identified as potential inhibitors, which might be suitable for targeting the different clinical forms of EGFR and dual inhibition of the studied drug targets to combat NSCLC. The result of this study suggest that these identified phytocompounds from garlic would serve as promising leads for the development of lead molecules to design new multi-targeting drugs to address the different clinical forms of NSCLC.

Exploration of Novel Inhibitors for Bruton's Tyrosine Kinase by 3D QSAR Modeling and Molecular Dynamics Simulation.

Bruton's tyrosine kinase (BTK) is a cytoplasmic, non-receptor tyrosine kinase which is expressed in most of the hematopoietic cells and plays an important role in many cellular signaling pathways. B cell malignancies are dependent on BCR signaling, thus making BTK an efficient therapeutic target. Over the last few years, significant efforts have been made in order to develop BTK inhibitors to treat B-cell malignancies, and autoimmunity or allergy/hypersensitivity but limited success has been achieved. Here in this study, 3D QSAR pharmacophore models were generated for Btk based on known IC50 values and experimental energy scores with extensive validations. The five features pharmacophore model, Hypo1, includes one hydrogen bond acceptor lipid, one hydrogen bond donor, and three hydrophobic features, which has the highest correlation coefficient (0.98), cost difference (112.87), and low RMS (1.68). It was further validated by the Fisher's randomization method and test set. The well validated Hypo1 was used as a 3D query to search novel Btk inhibitors with different chemical scaffold using high throughput virtual screening technique. The screened compounds were further sorted by applying ADMET properties, Lipinski's rule of five and molecular docking studies to refine the retrieved hits. Furthermore, molecular dynamic simulation was employed to study the stability of docked conformation and to investigate the binding interactions in detail. Several important hydrogen bonds with Btk were revealed, which includes the gatekeeper residues Glu475 and Met 477 at the hinge region. Overall, this study suggests that the proposed hits may be more effective inhibitors for cancer and autoimmune therapy.

Docking-enabled pharmacophore model for histone deacetylase 8 inhibitors and its application in anti-cancer drug discovery.

Zinc-dependent histone deacetylase 8 removes the epsilon-acetyl groups present in the N-terminal lysine residues of histone proteins, thereby restricting various transcription factors from being expressed. Inhibition of this enzyme has been reported to be a novel strategy in cancer treatment. To identify novel and diverse leads for use in potent histone deacetylase 8 inhibitor design, a pharmacophore model showing high correlation between experimental and estimated activities was generated using the best conformations of training set compounds from molecular docking experiments. The best pharmacophore model was validated using four different strategies and then used in database screening for novel virtual leads. Hit compounds were selected and subjected to molecular docking using GOLD. The top-scored compound was further optimized for improved binding. The optimization step led to a new set of compounds with both improved binding at the active site and estimated activities. The identified virtual leads could be used for designing potent histone deacetylase 8 inhibitors as anti-cancer therapeutics.

Molecular dynamics simulation study of valyl-tRNA synthetase with its pre- and post-transfer editing substrates.

The main role of aminoacyl-tRNA synthetases (aaRSs) is to transfer the cognate amino acids to the 3'-end of their tRNA by strictly discriminating from non-cognate amino acids. Some aaRSs accomplish this via proofreading and editing mechanisms, among which valyl-tRNA synthetase (ValRS) hydrolyses the non-cognate amino acid, threonine. In ValRS, existence of pre-transfer editing process is still unclear, although crystal structure of editing site with pre-transfer substrate analog (Thr-AMS) was released. In the case of isoleucyl-tRNA synthetase (IleRS), editing mechanism is well studied and mutational analyses revealed the existence of post- and pre-transfer editing mechanisms. Our aim is to investigate the possibility of pre-transfer editing process by performing molecular dynamics (MD) simulation studies. Simulations were carried out for ValRS with pre-transfer substrates (Thr-AMP/Val-AMP) and post-transfer substrates (Thr-A76/Val-A76) to understand their binding pattern. Two important point mutation studies were performed to observe their effect on editing process. This study also intends to compare and contrast the pre-transfer editing with post-transfer editing of ValRS. Interestingly, the MD simulation results revealed that non-cognate substrates (Thr-AMP/Thr-A76) bind more strongly than the cognate substrates (Val-AMP/Val-A76) in both pre- and post-transfer editing respectively. The editing site mutations (Lys270Ala and Asp279Ala) severely affected the binding ability of pre-transfer substrate (Thr-AMP) by different ways. Even though pre- and post-transfer substrates bind to the same site, specific differences were observed which has led us to believe the existence of the pre-transfer editing process in ValRS.