The profile and treatment outcomes of the older (aged 60 years and above) tuberculosis patients in Tamilnadu, South India

Background: With changing demographic patterns in the context of a high tuberculosis (TB) burden country, like India, there is very little information on the clinical and demographic factors associated with poor treatment outcome in the sub-group of older TB patients. The study aimed to assess the proportion of older TB patients (60 years of age and more), to compare the type of TB and treatment outcomes between older TB patients and other TB patients (less than 60 years of age) and to describe the demographic and clinical characteristics of older TB patients and assess any associations with TB treatment outcomes. Methods: A retrospective cohort study involving a review of records from April to June 2011 in the 12 selected districts of Tamilnadu, India. Demographic, clinical and WHO defined disease classifications and treatment outcomes of all TB patients aged 60 years and above were extracted from TB registers maintained routinely by Revised National TB Control Program (RNTCP). Results: Older TB patients accounted for 14% of all TB patients, of whom 47% were new sputum positive. They had 38% higher risk of unfavourable treatment outcomes as compared to all other TB patients (Relative risk (RR)-1.4, 95% CI 1.2–1.6). Among older TB patients, the risk for unfavourable treatment outcomes was higher for those aged 70 years and more (RR 1.5, 95% CI 1.2–1.9), males (RR 1.5, 95% CI 1.0–2.1), re-treatment patients (RR 2.5, 95% CI 1.9–3.2) and those who received community-based Direct Observed Treatment (RR 1.4, 95% CI 1.1–1.9). Conclusion: Treatment outcomes were poor in older TB patients warranting special attention to this group – including routine assessment and recording of co-morbidities, a dedicated recording, reporting and monitoring of outcomes for this age-group and collaboration with National programme of non-communicable diseases for comprehensive management of co-morbidities

Unintentional Passive Islanding Detection and Prevention Method with Reduced Non-Detection Zones

Islanding detection and prevention are involved in tandem with the rise of large- and small-scale distribution grids. To detect islanded buses, either the voltage or the frequency variation has been considered in the literature. A modified passive islanding detection strategy that coordinates the V-F (voltage–frequency) index was developed to reduce the non-detection zones (NDZs), and an islanding operation is proposed in this article. Voltage and frequency were measured at each bus to check the violation limits by implementing the proposed strategy. The power mismatch was alleviated in the identified islands by installing a battery and a diesel generator, which prevented islanding events. The proposed strategy was studied on the three distinct IEEE radial bus distribution systems, namely, 33-, 69-, and 118-bus systems. The results obtained in the above-mentioned IEEE bus systems were promising when the proposed strategy was implemented. The results of the proposed strategy were compared with those of methods developed in the recent literature. As a result, the detection time and number of islanded buses are reduced

An In Silico Explainable Multi-Parameter Optimization Approach for De Novo Drug Design Against Proteins from Central Nervous System

The aim of drug design and development is to produce a drug which can inhibit the target protein and possess a balanced physicochemical and toxicity profile. Traditionally, this is a multi-step process where different parameters such as activity, physicochemical and pharmacokinetic properties are optimized sequentially, which often leads to high attrition rate during later stages of drug design and development. We have developed a deep learning-based de novo drug design method which can design novel small molecules by optimizing target specificity as well as multiple parameters (including late-stage parameters) in a single step. All possible combinations of parameters were optimized to understand the effect of each parameter over the other parameters. An explainable predictive model was used to identify the molecular fragments responsible for the property being optimized. The proposed method was applied against the human 5-hydroxy tryptamine receptor 1B (5-HT1B), a protein from the central nervous system (CNS). Various physicochemical properties specific to CNS drugs were considered along with the target specificity and blood-brain barrier permeability (BBBP), which acts as an additional challenge for CNS drug delivery. The contribution of each parameter towards molecule design was identified by analyzing the properties of generated small molecules from optimization of all possible parameter combinations. The final optimized generative model was able to design similar inhibitors compared to known inhibitors of 5-HT1B. In addition, the functional groups of the generated small molecules that guide the BBBP predictive model were identified through feature attribution techniques

De Novo Design of New Chemical Entities (NCEs) for SARS-CoV-2 Using Artificial Intelligence

The novel SARS-CoV-2 is the source of a global pandemic COVID-19, which has severely affected the health and economy of several countries. Multiple studies are in progress, employing diverse approaches to design novel therapeutics against the potential target proteins in SARS-CoV-2. One of the well-studied protein targets for coronaviruses is the chymotrypsin-like (3CL) protease, responsible for post-translational modifications of viral polyproteins essential for its survival and replication in the host. There are ongoing attempts to repurpose the existing viral protease inhibitors against 3CL protease of SARS-CoV-2. Recent studies have proven the efficiency of artificial intelligence techniques in learning the known chemical space and generating novel small molecules. In this study, we employed deep neural network-based generative and predictive models for de novo design of new small molecules capable of inhibiting the 3CL protease. The generated small molecules were filtered and screened against the binding site of the 3CL protease structure of SARS-CoV-2. Based on the screening results and further analysis, we have identified 31 potential compounds as ideal candidates for further synthesis and testing against SARS-CoV-2. The generated small molecules were also compared with available natural products. Two of the generated small molecules showed high similarity to a plant natural product, Aurantiamide, which can be used for rapid testing during this time of crisis

Target-Specific Novel Molecules with their Recipe: Incorporating Synthesizability in the Design Process

Application of Artificial intelligence (AI) in drug discovery has led to several success stories in recent times. While traditional methods mostly relied upon screening large chemical libraries for early-stage drug-design, the AI-based approaches can help identify novel target-specific molecules by sampling from a much larger chemical space. Although this has increased the possibility of finding diverse and novel molecules from previously unexplored chemical space, this has also posed a great challenge for medicinal chemists to synthesize at least some of the AI-designed novel molecules for experimental validation. To address this challenge, in this work, we propose a novel forward synthesis-based generative AI method, which is used to explore the synthesizable chemical space. The method uses a structure-based drug design framework, where the target protein structure and a target-specific seed fragment from co-crystal structures can be the initial inputs. A random fragment from a purchasable fragment library can also be the input if a target-specific fragment is unavailable. Then a template-based forward synthesis route prediction and molecule generation is performed in parallel using the Monte Carlo Tree Search (MCTS) method where, the subsequent fragments for molecule growth can again be obtained from a purchasable fragment library. To the best of our knowledge, this is the first model to utilize MCTS for forward synthesis route prediction. The rewards for each iteration of MCTS are computed using a drug-target affinity (DTA) model based on the docking pose of the generated reaction intermediates at the binding site of the target protein of interest. With the help of the proposed method, it is now possible to overcome one of the major obstacles posed to the AI-based drug design approaches through the ability of the method to design novel target-specific synthesizable molecules

Mechanism of drug resistance in HIV-1 protease subtype C in the presence of Atazanavir

AIDS is one of the deadliest diseases in the history of humankind caused by HIV. Despite the technological development, curtailing the viral infection inside human host still remains a challenge. Therapies such as HAART uses a combination of drugs to inhibit the viral activity. One of the important targets includes HIV protease and inhibiting its activity will minimize the production of mature structural proteins. However, the genetic diversity and the occurrence of drug resistant mutations adds complexity to effective drug design. In this study, we aimed at understanding the drug binding mechanism of one such subtype, namely subtype C and its insertion variant L38HL. We performed multiple molecular dynamics simulations along with binding free energy analysis of wild-type and L38HL bound to Atazanavir (ATV). From the analysis, we revealed that the insertion alters the hydrogen bond and hydrophobic interaction networks. The alterations in the interaction networks increase flexibility at the hinge-fulcrum interface. Further, the effects of these changes affect flap tip curling. Moreover, the changes in the hinge-fulcrum-cantilever interface alters the concerted motion of the functional regions leading to change in the direction of flap movement thus causing a subtle change in the active site volume. Additionally, formation of intramolecular hydrogen bonds in the ATV docked to L38HL restricted the movement of R1 and R2 groups thereby altering the interactions. Overall, the changes in the flexibility of flap together with the changes in the active site volume and compactness of the ligand provide insights for increased binding affinity of ATV with L38HL

An Interpretable Machine Learning model for Selectivity of Small Molecules against Homologous Protein Family

Motivation: The primary goal of drug design is to develop potent small molecules that can inhibit the target protein with high selectivity. In the early stage of drug discovery, various experimental and computational methods are used to measure the target-specificity of small molecules against the target protein of interest. The selectivity of the small molecule remains a challenge, especially when the target protein belongs to a homologous family, which can often lead to off-target side effects. Results: We have developed a multi-task deep learning model for predicting the selectivity of small molecules on the closely related homologs of the target protein. The multi-task model, which can learn from training data of the related tasks has been tested on the Janus kinase (JAK) and dopamine receptor family of proteins. To decipher the model decision on selectivity, the important fragments associated with each homolog protein were identified using SHapley Additive exPlanations (SHAP) method. The performance of the multi-task model was evaluated using various representation of small molecules such as fingerprints (ECFP4) and molecular graph representations. It was observed that the feature-based representation (ECFP4) with the XGBoost performed marginally better when compared to deep neural network models in most of the evaluation metrics. Both the models outperformed the graph-based models. The identification of important fragments associated with each proteins of the homolog family using SHAP method, explains the factors that governed the decision of the multi-task predictive model. The proposed method can be used post hit generation