Repurposing existing drugs for new AMPK activators as a strategy to extend lifespan: a computer-aided drug discovery study

Candidate molecular targets uncovered in mouse lifespan extension studies

INTRODUCTION

Multiple interventions have demonstrated an increase in mouse lifespan. However, non-standardized controls, sex or strain-specific factors, and insufficient focus on targets, hinder the translation of these findings into clinical applications.

AREAS COVERED

We examined the effects of genetic and drug-based interventions on mice from databases DrugAge, GenAge, the Mouse Phenome Database, and publications from PubMed that led to a lifespan extension of more than 10%, identifying specific molecular targets that were manipulated to achieve the maximum lifespan in mice. Subsequently, we characterized 10 molecular targets influenced by these interventions, with particular attention given to clinical trials and potential indications for each.

EXPERT OPINION

To increase the translational potential of mice life-extension studies to clinical research several factors are crucial: standardization of mice lifespan research approaches, the development of clear criteria for control and experimental groups, the establishment of criteria for potential geroprotectors, and focusing on targets and their clinical application. Pinpointing the targets affected by geroprotectors helps in understanding species-specific differences and identifying potential side effects, ensuring the safety and effectiveness of clinical trials. Additionally, target review facilitates the optimization of treatment protocols and the evaluation of the clinical feasibility of translating research findings into practical therapies for humans.

Fusing Sequence and Structural Knowledge by Heterogeneous Models to Accurately and Interpretively Predict Drug-Target Affinity

Drug-target affinity (DTA) prediction is crucial for understanding molecular interactions and aiding drug discovery and development. While various computational methods have been proposed for DTA prediction, their predictive accuracy remains limited, failing to delve into the structural nuances of interactions. With increasingly accurate and accessible structure prediction of targets, we developed a novel deep learning model, named S2DTA, to accurately predict DTA by fusing sequence features of drug SMILES, targets, and pockets and their corresponding graph structural features using heterogeneous models based on graph and semantic networks. Experimental findings underscored that complex feature representations imparted negligible enhancements to the model's performance. However, the integration of heterogeneous models demonstrably bolstered predictive accuracy. In comparison to three state-of-the-art methodologies, such as DeepDTA, GraphDTA, and DeepDTAF, S2DTA's performance became more evident. It exhibited a 25.2% reduction in mean absolute error (MAE) and a 20.1% decrease in root mean square error (RMSE). Additionally, S2DTA showed some improvements in other crucial metrics, including Pearson Correlation Coefficient (PCC), Spearman, Concordance Index (CI), and R2, with these metrics experiencing increases of 19.6%, 17.5%, 8.1%, and 49.4%, respectively. Finally, we conducted an interpretability analysis on the effectiveness of S2DTA by bidirectional self-attention mechanism. The analysis results supported that S2DTA was an effective and accurate tool for predicting DTA.

DeepDTAF: a deep learning method to predict protein-ligand binding affinity

Biomolecular recognition between ligand and protein plays an essential role in drug discovery and development. However, it is extremely time and resource consuming to determine the protein-ligand binding affinity by experiments. At present, many computational methods have been proposed to predict binding affinity, most of which usually require protein 3D structures that are not often available. Therefore, new methods that can fully take advantage of sequence-level features are greatly needed to predict protein-ligand binding affinity and accelerate the drug discovery process. We developed a novel deep learning approach, named DeepDTAF, to predict the protein-ligand binding affinity. DeepDTAF was constructed by integrating local and global contextual features. More specifically, the protein-binding pocket, which possesses some special properties for directly binding the ligand, was firstly used as the local input feature for protein-ligand binding affinity prediction. Furthermore, dilated convolution was used to capture multiscale long-range interactions. We compared DeepDTAF with the recent state-of-art methods and analyzed the effectiveness of different parts of our model, the significant accuracy improvement showed that DeepDTAF was a reliable tool for affinity prediction. The resource codes and data are available at https: //github.com/KailiWang1/DeepDTAF.

An overview of drug discovery and development

A new medicine will take an average of 10-15 years and more than US$2 billion before it can reach the pharmacy shelf. Traditionally, drug discovery relied on natural products as the main source of new drug entities, but was later shifted toward high-throughput synthesis and combinatorial chemistry-based development. New technologies such as ultra-high-throughput drug screening and artificial intelligence are being heavily employed to reduce the cost and the time of early drug discovery, but they remain relatively unchanged. However, are there other potentially faster and cheaper means of drug discovery? Is drug repurposing a viable alternative? In this review, we discuss the different means of drug discovery including their advantages and disadvantages.

Rational drug repurposing for cancer by inclusion of the unbiased molecular dynamics simulation in the structure-based virtual screening approach: Challenges and breakthroughs

Managing cancer is now one of the biggest concerns of health organizations. Many strategies have been developed in drug discovery pipelines to help rectify this problem and two of the best ones are drug repurposing and computational methods. The combination of these approaches can have immense impact on the course of drug discovery. In silico drug repurposing can significantly reduce the time, the cost and the effort of drug development. Computational methods such as structure-based drug design (SBDD) and virtual screening can predict the potentials of small molecule binders, such as drugs, for having favorable effect on a particular molecular target. However, the demand for accuracy and efficiency of SBDD requires more sophisticated and complicated approaches such as unbiased molecular dynamics (UMD) simulation that has been recently introduced. As a complementary strategy, the knowledge acquired from UMD simulations can increase the chance of finding the right candidates and the pipeline of its administration is introduced and discussed in this review. An elaboration of this pipeline is also made by detailing an example, the binding and unbinding pathways of dasatinib-c-Src kinase complex, which shows that how influential this method can be in rational drug repurposing in cancer treatment.

Naive extrapolations, overhyped claims and empty promises in ageing research and interventions need avoidance

Most proclamations about another wonder breakthrough and another imminent miracle treatment of ageing are usually overhyped claims and empty promises. It is not that the experimental science behind those claims is totally wrong or fake. But it is often a case of being ahistorical and ignoring the cumulated knowledge and understanding of the evolutionary and biological principles of ageing and longevity. Furthermore, remaining stuck to the body-as-a-machine viewpoint reduces ageing and its associated health challenges to a mere problem of engineering and design. However, highly dynamic nature of the living systems with properties of interaction, interdependence, tolerance, adaptation and constant remodelling requires wholistic and interactive modes of understanding and maintaining health. The physiological relevance and significance of progressively accumulating molecular damage remains to be fully understood. As for ageing interventions, the three pillars of health-food, physical activity, and social and mental engagement-which actually show health-promoting effect, cannot simply be reduced to a single or a limited number of molecular targets with hopes of creating an exercise pill, a fasting pill, a happiness pill and so on. If we want to increase the credibility and socio-political-economic support of ageing research and interventions, we need to resist the temptation to overhype the claims or to make far-fetched promises, which undermine the theoretical and practical significance of new discoveries in biogerontology.

Screening Bioactive Compounds of Siraitia grosvenorii by Immobilized β2-Adrenergic Receptor Chromatography and Druggability Evaluation

As the first and key step of traditional Chinese medicine (TCM)-guided drug development, lead discovery necessitates continuous exploration of new methodology for screening bioactive compounds from TCM. This work intends to establish a strategy for rapidly recognizing β2-adrenergic receptor (β2-AR) target compounds from the fruit of Siraitia grosvenorii (LHG). The method involved immobilization of β2-AR onto amino-microsphere to synthesize the receptor column, the combination of the column to high-performance liquid chromatography (HPLC) to screen bioactive compounds of LHG, the identification of the compounds by HPLC coupled with mass spectrometry (MS), and the evaluation of druggability through pharmacokinetic examination by HPLC-MS/MS. Mogroside V was screened and identified as the β2-AR-targeted bioactive compounds in LHG. This compound exhibited desired pharmacokinetic behavior including the time to reach peak plasma concentrations of 45 min, the relatively low elimination of 138.5 min, and the high bioavailability. These parameters indicated that mogroside V has a good druggability for the development of new drugs fighting β2-AR-mediated respiratory ailments like asthma. The combination of the methods in this work is probably becoming a powerful strategy for screening and early evaluating the bioactive compounds specifically binding to G-protein-coupled receptor target from complex matrices including TCM.

Hierarchical Virtual Screening of Potential Insectides Inhibitors of Acetylcholinesterase and Juvenile Hormone from Temephos

Aedes aegypti (Linnaeus, 1762; Diptera: Culicidae) is the main vector transmitting viral diseases such as dengue fever, dengue haemorrhagic fever, urban yellow fever, zika and chikungunya. Worldwide, especially in the Americas and Brazil, many cases of dengue have been reported in recent years, which have shown significant growth. The main control strategy is the elimination of the vector, carried out through various education programs, to change human habits, but the most usual is biological control, together with environmental management and chemical control. The most commonly insecticide used is temephos (an organophosphorus compound), but Aedes aegypti populations have shown resistance and the product is highly toxic, so we chose it as a template molecule to perform a ligand-based virtual screening in the ChemBrigde (DIVERSet-CL subcollection) database, searching for derivatives with similarity in shape (ROCS) and electrostatic potential (EON). Thus, fourty-five molecules were filtered based on their pharmacokinetic and toxicological properties and 11 molecules were selected by a molecular docking study, including binding affinity and mode of interaction. The L46, L66 and L68 molecules show potential inhibitory activity for both the insect (−9.28, −10.08 and −6.78 Kcal/mol, respectively) and human (−6.05, 6.25 and 7.2 Kcal/mol respectively) enzymes, as well as the juvenile hormone protein (−9.2; −10.96 and −8.16 kcal/mol, respectively), showing a significant difference in comparison to the template molecule temephos. Molecules L46, L66 and L68 interacted with important amino acids at each catalytic site of the enzyme reported in the literature. Thus, the molecules here investigated are potential inhibitors for both the acetylcholinesterase enzymes and juvenile hormone protein–from insect and humans, characterizing them as a potential insecticide against the Aedes aegypti mosquito.

Identifying Potential Ageing-Modulating Drugs In Silico

Increasing human life expectancy has posed increasing challenges for healthcare systems. As people age, they become more susceptible to chronic diseases, with an increasing burden of multimorbidity, and the associated polypharmacy. Accumulating evidence from work with laboratory animals has shown that ageing is a malleable process that can be ameliorated by genetic and environmental interventions. Drugs that modulate the ageing process may delay or even prevent the incidence of multiple diseases of ageing. To identify novel, anti-ageing drugs, several studies have developed computational drug-repurposing strategies. We review published studies showing the potential of current drugs to modulate ageing. Future studies should integrate current knowledge with multi-omics, health records, and drug safety data to predict drugs that can improve health in late life.

Performing an In Silico Repurposing of Existing Drugs by Combining Virtual Screening and Molecular Dynamics Simulation

Drug repurposing has become one of the most widely used methods that can make drug discovery more efficient and less expensive. Additionally, computational methods such as structure-based drug designing can be utilized to make drug discovery more efficient and more accurate. Now imagine what can be achieved by combining drug repurposing and computational methods together in drug discovery, "in silico repurposing." In this chapter, we tried to describe a method that combines structure-based virtual screening and molecular dynamics simulation which can find effective compounds among existing drugs that may affect on a specific molecular target. By using molecular docking as a tool for the screening process and then by calculating ligand binding in an active receptor site using scoring functions and inspecting the proper orientation of pharmacophores in the binding site, the potential compounds will be chosen. After that, in order to test the potential compounds in a realistic environment, molecular dynamics simulation and related analysis have to be carried out for separating the false positives and the true positives from each other and finally identifying true "Hit" compounds. It's good to emphasize that if any of these identified potential compounds turn out to have the efficacy to affect that specific molecular target, it can be taken to the phase 2 clinical trials straightaway.