Identification of mycobacterial Thymidylate kinase inhibitors: a comprehensive pharmacophore, machine learning, molecular docking, and molecular dynamics simulation studies.

Thymidylate kinase (TMK) is a pivotal enzyme in Mycobacterium tuberculosis (Mtb), crucial for phosphorylating thymidine monophosphate (dTMP) to thymidine diphosphate (dTDP), thereby playing a critical role in DNA biosynthesis. Dysregulation or inhibition of TMK activity disrupts DNA replication and cell division, making it an attractive target for anti-tuberculosis drug development. In this study, the statistically validated pharmacophore mode was developed from a set of known TMK inhibitors. Further, the robust pharmacophore was considered for screening the Enamine database. The chemical space was reduced through multiple molecular docking approaches, pharmacokinetics, and absolute binding energy estimation. Two different molecular docking algorithms favor the strong binding affinity of the proposed molecules towards TMK. Machine learning-based absolute binding energy also showed the potentiality of the proposed molecules. The binding interactions analysis exposed the strong binding affinity between the proposed molecules and active site amino residues of TMK. Several statistical parameters from all atoms MD simulation explained the stability between proposed molecules and TMK in the dynamic states. The MM-GBSA approach also found a strong binding affinity for each proposed molecule. Therefore, the proposed molecules might be crucial TMK inhibitors for managing Mtb inhibition subjected to in vitro/in vivo validations.

Identification of novel hit molecules targeting M. tuberculosis polyketide synthase 13 by combining generative AI and physics-based methods.

In this work we investigated the Pks13-TE domain, which plays a critical role in the viability of the mycobacteria. In this report, we have used a series of AI and Physics-based tools to identify Pks13-TE inhibitors. The Reinvent 4, pKCSM, KDeep, and SwissADME are AI-ML-based tools. AutoDock Vina, PLANTS, MDS, and MM-GBSA are physics-based methods. A combination of these methods yields powerful support in the drug discovery cycle. Known inhibitors of Pks13-TE were collected, curated, and used as input for the AI-based tools, and Mol2Mol molecular optimisation methods generated novel inhibitors. These ligands were filtered based on physics-based methods like molecular docking and molecular dynamics using multiple tools for consensus generation. Rigorous analysis was performed on the selected compounds to reduce the chemical space while retaining the most promising compounds. The molecule interactions, stability of the protein-ligand complexes and the comparable binding energies with the native ligand were essential factors for narrowing the ligands set. The filtered ligands from docking, MDS, and binding energy colocations were further tested for their ADMET properties since they are among the essential criteria for this series of molecules. It was found that ligands Mt1 to Mt6 have excellent predicted pharmacokinetic, pharmacodynamic and toxicity profiles and good synthesisability.

Machine learning assisted methods for the identification of low toxicity inhibitors of Enoyl-Acyl Carrier Protein Reductase (InhA).

Tuberculosis (TB) is one of the life-threatening infectious diseases with prehistoric origins and occurs in almost all habitable parts of the world. TB mainly affects the lungs, and its etiological agent is Mycobacterium tuberculosis (Mtb). In 2022, more than 10 million people were infected worldwide, and 1.3 million were children. The current study considered the in-silico and machine learning (ML) approaches to explore the potential anti-TB molecules from the SelleckChem database against Enoyl-Acyl Carrier Protein Reductase (InhA). Initially, the entire database of ∼ 119000 molecules was sorted out through drug-likeness. Further, the molecular docking study was conducted to reduce the chemical space. The standard TB drug molecule's binding energy was considered a threshold, and molecules found with lower affinity were removed for further analyses. Finally, the molecules were checked for the pharmacokinetic and toxicity studies, and compounds found to have acceptable pharmacokinetic parameters and were non-toxic were considered as final promising molecules for InhA. The above approach further evaluated five molecules for ML-based toxicity and synthetic accessibility assessment. Not a single molecule was found toxic and each of them was revealed as easy to synthesise. The complex between InhA and proposed and standard molecules was considered for molecular dynamics simulation. Several statistical parameters showed the stability between InhA and the proposed molecule. The high binding affinity was also found for each of the molecules towards InhA using the MM-GBSA approach. Hence, the above approaches and findings exposed the potentiality of the proposed molecules against InhA.

Identification of Mycobacterium tuberculosis transcriptional repressor EthR inhibitors: Shape-based search and machine learning studies.

Tuberculosis has been a challenge to the world since prehistoric times, and with the advent of drug-resistant strains, it has become more challenging to treat this infection. Ethionamide (ETH), a second-line drug, acts as a prodrug and targets mycolic acid synthesis by targeting the enoyl-acyl carrier protein reductase (InhA) enzyme. Mycobacterium tuberculosis (Mtb) EthR is an ethA gene repressor required to activate prodrug ETH. Recent studies suggest targeting the EthR could lead to newer drug molecules that would help better activate the ETH or complement this process. In this report, we have attempted and successfully identified three new molecules from the drug repurposing library that can target EthR protein and function as ETH boosters. These molecules were obtained after rigorous filtering of the database for their physicochemical, toxicological properties and safety. The molecular docking, molecular dynamics simulations and binding energy studies yielded three compounds, Ethyl (2-amino-4-((4-fluorobenzyl)amino)phenyl)carbamate) (L1), 2-((2,2-Difluorobenzo [d] [1,3]dioxol-5-yl)amino)-2-oxoethyl (E)-3-(5-bromofuran-2-yl)acrylate (L2), and N-(2,3-Dihydrobenzo [b] [1,4]dioxin-6-yl)-4-(2-((4-fluorophenyl)amino)-2-oxoethoxy)-3-methoxy benzamide (L3) are potential EthR inhibitors. We applied machine learning methods to evaluate these molecules for toxicity and synthesisability, suggesting safety and ease of synthesis for these molecules. These molecules are known for other pharmacological activities and can be repurposed faster as adjuvant therapy for tuberculosis.

Platelet-to-albumin ratio and radiation-induced lymphopenia-prognostic biomarker for carcinoma esophagus.

BACKGROUND: Esophageal cancer has a poor survival outcome with 5-year OS at 16.7% despite treatment. Some inflammation-based prognostic indicators like the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) have been previously studied as potential biomarker for predicting outcome in esophageal cancer. Recently, platelet-to-albumin ratio (PAR) has been reported as a promising prognostic factor in gastrointestinal malignancies. METHODS: We performed a retrospective analysis of prospectively treated patients of carcinoma esophagus to evaluate the prognostic significance of inflammation-based prognostic indicators-neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and a composite inflammation-nutrition index: platelet-to-albumin ratio (PAR) in esophageal cancer. Based on previous studies, the optimal cut-off value of PAR was kept at 5.7 × 10^9, and 2.62 for NLR. RESULTS: A total of 71 patients of locally advanced esophageal cancer treated between 2019 and 2022, with either neoadjuvant or definitive chemoradiotherapy, were included. Median follow-up time was 19 months [range: 7-44 months]. Median OS and PFS in our study cohort were 11.3 months [range: 7-23 months] and 7.8 months [range: 3-17 months], respectively. In univariate analysis, lower PAR was found to be significantly correlated with shorter survival time (HR = 2.41; 1.3-4.76; p = 0.047). There was no association found between the OS and the NLR [HR = 1.09; 0.95-1.26; p = 0.222]. Univariate and multivariate linear and logistic regressions found no association between V15, V10, V5, or V2 of spleen and nadir lymphocyte count or between Dmax or Dmean and nadir lymphocyte counts. CONCLUSION: Present analysis found a trend toward an inverse association between PAR and OS. PAR, in the not-so-distant future, may evolve as a novel, convenient, and inexpensive prognostic indicator in esophageal cancer.

Anticancer Drug Discovery Based on Natural Products: From Computational Approaches to Clinical Studies.

Globally, malignancies cause one out of six mortalities, which is a serious health problem. Cancer therapy has always been challenging, apart from major advances in immunotherapies, stem cell transplantation, targeted therapies, hormonal therapies, precision medicine, and palliative care, and traditional therapies such as surgery, radiation therapy, and chemotherapy. Natural products are integral to the development of innovative anticancer drugs in cancer research, offering the scientific community the possibility of exploring novel natural compounds against cancers. The role of natural products like Vincristine and Vinblastine has been thoroughly implicated in the management of leukemia and Hodgkin's disease. The computational method is the initial key approach in drug discovery, among various approaches. This review investigates the synergy between natural products and computational techniques, and highlights their significance in the drug discovery process. The transition from computational to experimental validation has been highlighted through in vitro and in vivo studies, with examples such as betulinic acid and withaferin A. The path toward therapeutic applications have been demonstrated through clinical studies of compounds such as silvestrol and artemisinin, from preclinical investigations to clinical trials. This article also addresses the challenges and limitations in the development of natural products as potential anti-cancer drugs. Moreover, the integration of deep learning and artificial intelligence with traditional computational drug discovery methods may be useful for enhancing the anticancer potential of natural products.

Investigation of bio-active Amaryllidaceae alkaloidal small molecules as putative SARS-CoV-2 main protease and host TMPRSS2 inhibitors: interpretation by in-silico simulation study.

The novel coronavirus disease 2019 (Covid-19) outburst is still threatening global health. This highly contagious viral disease is caused by the infection of SARS-CoV-2 virus. Covid-19 and post-Covid-19 complications induce noteworthy mortality. Potential chemical hits and leads against SARS-CoV-2 for combating Covid-19 are urgently required. In the present study, a virtual-screening protocol was executed on potential Amaryllidaceae alkaloids from a pool of natural compound library against SARS-CoV-2 main protease (Mpro) and transmembrane serine protease (TMPRSS2). For the collected 1016 alkaloids from the curated library, initially, molecular docking using AutoDock Vina (ADV), and thereafter 100 ns molecular-dynamic (MD) simulation has been executed for the best top-ranked binding affinity compounds for both the viral and host proteins. Comprehensive intermolecular-binding interactions profile of Amaryllidaceae alkaloids suggested that phyto-compounds Galantamine, Lycorenine, and Neronine as potent modulators of SARS-CoV-2 Mpro and host TMPRSS2 protein. All atomistic long range 100 ns MD simulation studies of each top ranked complex in triplicates also illustrated strong binding affinity of three compounds towards Mpro and TMPRSS2. Identified compounds might be recommended as prospective anti-viral agents for future drug development selectively targeting the SARS-CoV-2 Mpro or blocking host TMPRSS2 receptor, subjected to pre-clinical and clinical assessment for a better understanding of in-vitro molecular interaction and in-vivo validation.Communicated by Ramaswamy H. Sarma.

Identification of bio-active food compounds as potential SARS-CoV-2 PLpro inhibitors-modulators via negative image-based screening and computational simulations.

Despite significant studies on the COVID-19 pandemic, scientists around the world are still battling to find a definitive therapy against the ongoing severe global health crisis. In this study, advanced computational approaches have been employed to identify bioactive food constituents as potential SARS-CoV-2 PLpro inhibitors-modulators. As a validated antiviral drug target, PLpro has gained tremendous attention for therapeutics developments. Therefore, targeting the multifunctional SARS-CoV-2 PLpro protein, ∼1039 bioactive dietary compounds have been screened extensively through novel techniques like negative image-based (NIB) screening and molecular docking approaches. In particular, the three different models of NIB screening have been generated and used to re-score the dietary compounds based on the negative image which is created by reversing the shape and electrostatics features of PLpro protein's ligand-binding cavity. Further, 100 ns molecular dynamics simulation has been performed and MM-GBSA based binding free energies have been estimated for the final proposed four dietary compounds (PC000550, PC000361, PC000558, and PC000573) as potential inhibitors/modulators of SARS-CoV-2 PLpro protein. Employed computational study outcome also has been compared with respect to the earlier experimentally investigated compound GRL0617 against SARS-CoV-2 PLpro protein, which suggests much greater interaction potential in terms of binding affinity and other energetic contributions for the proposed dietary compounds. Hence, the present study suggests that proposed dietary compounds can be suitable chemical entities for modulating the activity of PLpro protein or can be further utilized for optimizing or screening of novel chemical surrogates, however also needs experimental evaluation for entry in clinical studies for better assessment.

Low-dose radiation therapy for COVID-19 pneumonia: a pilot study.

OBJECTIVES: The World Health Organization (WHO) has declared coronavirus disease 2019 (COVID-19) as pandemic in March 2020. Currently there is no specific effective treatment for COVID-19. The major cause of death in COVID-19 is severe pneumonia leading to respiratory failure. Radiation in low doses (<100 cGy) has been known for its anti-inflammatory effect and therefore, low dose radiation therapy (LDRT) to lungs can potentially mitigate the severity of pneumonia and reduce mortality. We conducted a pilot trial to study the feasibility and clinical efficacy of LDRT to lungs in the management of patients with COVID-19. METHODS: From June to Aug 2020, we enrolled 10 patients with COVID-19 having moderate to severe risk disease [National Early Warning Score (NEWS) of ≥5]. Patients were treated as per the standard COVID-19 management guidelines along with LDRT to both lungs with a dose of 70cGy in single fraction. Response assessment was done based on the clinical parameters using the NEWS. RESULTS: All patients completed the prescribed treatment. Nine patients had complete clinical recovery mostly within a period ranging from 3 to 7 days. One patient, who was a known hypertensive, showed clinical deterioration and died 24 days after LDRT. No patients showed the signs of acute radiation toxicity. CONCLUSION: The results of our pilot study suggest that LDRT is feasible in COVID-19 patients having moderate to severe disease. Its clinical efficacy may be tested by conducting randomized controlled trials. ADVANCES IN KNOWLEDGE: LDRT has shown promising results in COVID-19 pneumonia and should be researched further through randomized controlled trials.

Multi-step molecular docking and dynamics simulation-based screening of large antiviral specific chemical libraries for identification of Nipah virus glycoprotein inhibitors.

Nipah virus (NiV) infections are highly contagious and can cause severe febrile encephalitis. An outbreak of NiV infection has reported high mortality rates in Southeast Asian countries including Bangladesh, East Timor, Malaysia, Papua New Guinea, Vietnam, Cambodia, Indonesia, Madagascar, Philippines, Thailand and India. Considering the high risk for an epidemic outbreak, the World Health Organization (WHO) declared NiV as an emerging priority pathogen. However, there are no effective therapeutics or any FDA approved drugs available for the treatment of this infection. Among the known nine proteins of NiV, glycoprotein plays an important role in initiating the entry of viruses and attaching to the host cell receptors. Herein, three antiviral databases consisting of 79,892 chemical entities have been computationally screened against NiV glycoprotein (NiV-G). Particularly, multi-step molecular docking followed by extensive molecular binding interactions analyses, binding free energy estimation, in silico pharmacokinetics, synthetic accessibility and toxicity profile evaluations have been carried out for initial identification of potential NiV-G inhibitors. Further, molecular dynamics (MD) simulation has been performed to understand the dynamic properties of NiV-G protein-bound with proposed five inhibitors (G1-G5) and their interactions behavior, and any conformational changes in NiV-G protein during simulations. Moreover, Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) based binding free energies (∆G) has been calculated from all MD simulation trajectories to understand the energy contribution of each proposed compound in maintaining and stabilizing the complex binding interactions with NiV-G protein. Proposed compounds showed high negative ∆G values ranging from -166.246 to -226.652 kJ/mol indicating a strong affinity towards the NiV-G protein.

Pharmacoinformatics approach based identification of potential Nsp15 endoribonuclease modulators for SARS-CoV-2 inhibition.

In the current study, a structure-based virtual screening paradigm was used to screen a small molecular database against the Non-structural protein 15 (Nsp15) endoribonuclease of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The SARS-CoV-2 is the causative agent of the recent outbreak of coronavirus disease 2019 (COVID-19) which left the entire world locked down inside the home. A multi-step molecular docking study was performed against antiviral specific compounds (~8722) collected from the Asinex antiviral database. The less or non-interacting molecules were wiped out sequentially in the molecular docking. Further, MM-GBSA based binding free energy was estimated for 26 compounds which shows a high affinity towards the Nsp15. The drug-likeness and pharmacokinetic parameters of all 26 compounds were explored, and five molecules were found to have an acceptable pharmacokinetic profile. Overall, the Glide-XP docking score and Prime-MM-GBSA binding free energy of the selected molecules were explained strong interaction potentiality towards the Nsp15 endoribonuclease. The dynamic behavior of each molecule with Nsp15 was assessed using conventional molecular dynamics (MD) simulation. The MD simulation information was strongly favors the Nsp15 and each identified ligand stability in dynamic condition. Finally, from the MD simulation trajectories, the binding free energy was estimated using the MM-PBSA method. Hence, the proposed final five molecules might be considered as potential Nsp15 modulators for SARS-CoV-2 inhibition.

A phase III randomised trial of trans-abdominal ultrasound in improving application quality and dosimetry of intra-cavitary brachytherapy in locally advanced cervical cancer.

INTRODUCTION: Intra-cavitary brachytherapy (ICB) remains an integral part of radiotherapy treatment in cervical cancer. Two-dimensional X ray point-based planning remains common and blind insertion leads to uterine perforations and higher toxicity. We conducted a randomised controlled trial of using trans-abdominal ultrasound in performing ICB to reduce perforation and organ at risk doses. PATIENT AND METHODS: The present study is a phase III open label randomised controlled trial of ultrasound guided ICB conducted on invasive cervical cancer patients. Patients were randomised by a simple computer-generated randomization chart into Arm A (No Ultrasound guidance) and Arm B (ICB with ultrasound guidance). The uterine perforation rates, tandem length change rates, bladder doses, rectal dose and procedure times were compared. Fischer exact test was used to compare the arms and p value <0.05 considered significant. RESULTS: A total of 160 patients were randomised. With US assistance, the uterine perforation rate was 1.25% (n = 1). In the non-US assistance arm the perforation rate was 12.5% (n = 10) (p = 0.005). Mean time to complete the entire procedure was significantly shortened from 26 min to 19 min favouring the US arm (p = 0.001). Dosimetric assessment between the two groups showed significant decrease in dose received by the various organs at risk with US assistance. CONCLUSION: The present study confirms significant improvement in application quality as well as dosimetry with reduction in procedure time. Trans-Abdominal US should be routinely used for ICB procedures, particularly in resource limited settings.

An analysis of non-cultivable bacteria using WEKA.

The study of metagenomics from high throughput sequencing data processed through Waikato Environment for Knowledge Analysis (WEKA) is gaining momentum in recent years. Therefore, we report an analysis of metagenome data generated using T-RFLP followed by using the SMO (Sequential minimal optimization) algorithm in WEKA to identify the total amount of cultured and uncultured microorganism present in the sample collected from multiple sources.

De novo design based identification of potential HIV-1 integrase inhibitors: A pharmacoinformatics study.

In the present study, pharmacoinformatics paradigms include receptor-based de novo design, virtual screening through molecular docking and molecular dynamics (MD) simulation are implemented to identify novel and promising HIV-1 integrase inhibitors. The de novodrug/ligand/molecule design is a powerful and effective approach to design a large number of novel and structurally diverse compounds with the required pharmacological profiles. A crystal structure of HIV-1 integrase bound with standard inhibitor BI-224436 is used and a set of 80,000 compounds through the de novo approach in LigBuilder is designed. Initially, a number of criteria including molecular docking, in-silico toxicity and pharmacokinetics profile assessments are implied to reduce the chemical space. Finally, four de novo designed molecules are proposed as potential HIV-1 integrase inhibitors based on comparative analyses. Notably, strong binding interactions have been identified between a few newly identified catalytic amino acid residues and proposed HIV-1 integrase inhibitors. For evaluation of the dynamic stability of the protein-ligand complexes, a number of parameters are explored from the 100 ns MD simulation study. The MD simulation study suggested that proposed molecules efficiently retained their molecular interaction and structural integrity inside the HIV-1 integrase. The binding free energy is calculated through the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) approach for all complexes and it also explains their thermodynamic stability. Hence, proposed molecules through de novo design might be critical to inhibiting the HIV-1 integrase.

Identification of potential cruzain inhibitors using de novo design, molecular docking and dynamics simulations studies.

Communicated by Ramaswamy H. Sarma.

Pharmacoinformatics-based identification of anti-bacterial catalase-peroxidase enzyme inhibitors.

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb). In the present age, due to the rapid increase in antibiotic resistance worldwide, TB has become a major threat to human life. Regardless of significant efforts have been inclined to improve the healthcare systems for improving diagnosis, treatment, and anticipatory measures controlling TB is challenging. To date, there are no such therapeutic chemical agents available to fight or control the bacterial drug-resistance. The catalase-peroxidase enzyme (katG) which encoded by the katG gene of Mtb is most frequently getting mutated and hence promotes Isoniazid resistance by diminishing the normal activity of katG enzyme. In the current study, an effort has been intended to find novel and therapeutically active antibacterial chemical compounds through pharmacoinformatics methodologies. Initially, the five mutant katG were generated by making mutation of Ser315 by Thr, Ile, Arg, Asn, and Gly followed by structural optimizations. About eight thousand small molecules were collected from the Asinex antibacterial library. All molecules were docked to active site of five mutant katG and wild type katG. To narrow down the chemical space several criteria were imposed including, screening for highest binding affinity towards katG proteins, compounds satisfying various criterion of drug-likeliness properties like Lipinski's rule of five (RO5), Veber's rule, absorption, distribution, metabolism, and excretion (ADME) profile, and synthetic accessibility. Finally, five molecules were found to be important antibacterial katG inhibitors. All the analyzed parameters suggested that selected molecules are promising in nature. Binding interactions analysis revealed that proposed molecules are efficient enough to form a number of strong binding interactions with katG proteins. Dynamic behavior of the proposed molecules with katG protein was evaluated through 100 ns molecular dynamics (MD) simulation study. Parameters calculated from the MD simulation trajectories adjudged that all molecules can form stable complexes with katG. High binding free energy of all proposed molecules definitely suggested strong affection towards the katG. Hence, it can be concluded that proposed molecules might be used as antibacterial chemical component subjected to experimental validation.