To explore the pharmacological mechanism of action using digital twin

With the advent of medical technology and science, the number of animals used in research has increased. For decades, the use of animals in research and product testing has been a point of conflict. Experts and pharmaceutical manufacturers are harming animals worldwide during laboratory research. Animals have also played a significant role in the advancement of science; animal testing has enabled the discovery of various novel drugs. The misery, suffering, and deaths of animals are not worth the potential human benefits. As a result, animals must not be exploited in research to assess the drug mechanism of action (MOA). Apart from the ethical concern, animal testing has a few more downsides, including the requirement for skilled labor, lengthy processes, and cost. Because it is critical to investigate adverse effects and toxicities in the development of potentially viable drugs. Assessment of each target will consume the range of resources as well as disturb living nature. As the digital twin works in an autonomous virtual world without influencing the physical structure and biological system. Our proposed framework suggests that the digital twin is a great reliable model of the physical system that will be beneficial in assessing the possible MOA prior to time without harming animals. The study describes the creation of a digital twin to combine the information and knowledge obtained by studying the different drug targets and diseases. Mechanism of Action using Digital twin (MOA-DT) will enable the experts to use an innovative approach without physical testing to save animals, time, and resources. DT reflects and simulates the actual drug and its relationships with its target, however presenting a more accurate depiction of the drug, which leads to maximize efficacy and decrease the toxicity of a drug. In conclusion, it has been shown that drug discovery and development can be safe, effective, and economical in no time through the combination of the digital and physical models of a pharmaceutical as compared to experimental animals.

Biochemical Properties of Two Plasmodium malariae Cysteine Proteases, Malapain-2 and Malapain-4

Cysteine proteases belonging to the falcipain (FP) family play a pivotal role in the biology of malaria parasites and have been extensively investigated as potential antimalarial drug targets. Three paralogous FP-family cysteine proteases of Plasmodium malariae, termed malapains 2–4 (MP2–4), were identified in PlasmoDB. The three MPs share similar structural properties with the FP-2/FP-3 subfamily enzymes and exhibit a close phylogenetic lineage with vivapains (VXs) and knowpains (KPs), FP orthologues of P. vivax and P. knowlesi. Recombinant MP-2 and MP-4 were produced in a bacterial expression system, and their biochemical properties were characterized. Both recombinant MP-2 and MP-4 showed enzyme activity across a broad range of pH values with an optimum activity at pH 5.0 and relative stability at neutral pHs. Similar to the FP-2/FP-3 subfamily enzymes in other Plasmodium species, recombinant MP-2 and MP-4 effectively hydrolyzed hemoglobin at acidic pHs. They also degraded erythrocyte cytoskeletal proteins, such as spectrin and band 3, at a neutral pH. These results imply that MP-2 and MP-4 are redundant hemoglobinases of P. malariae and may also participate in merozoite egression by degrading erythrocyte cytoskeletal proteins. However, compared with other FP-2/FP-3 enzymes, MP-2 showed a strong preference for arginine at the P2 position. Meanwhile, MP-4 showed a primary preference for leucine at the P2 position but a partial preference for phenylalanine. These different substrate preferences of MPs underscore careful consideration in the design of optimized inhibitors targeting the FP-family cysteine proteases of human malaria parasites.

LncRNAs and the Angiogenic Switch in Cancer: Clinical Significance and Therapeutic Opportunities

Angiogenesis is one of the hallmarks of cancer, and the establishment of new blood vessels is vital to allow for a tumour to grow beyond 1–2 mm in size. The angiogenic switch is the term given to the point where the number or activity of the pro-angiogenic factors exceeds that of the anti-angiogenic factors, resulting in the angiogenic process proceeding, giving rise to new blood vessels accompanied by increased tumour growth, metastasis, and potential drug resistance. Long noncoding ribonucleic acids (lncRNAs) have been found to play a role in the angiogenic switch by regulating gene expression, transcription, translation, and post translation modification. In this regard they play both anti-angiogenic and pro-angiogenic roles. The expression levels of the pro-angiogenic lncRNAs have been found to correlate with patient survival. These lncRNAs are also potential drug targets for the development of therapies that will inhibit or modify tumour angiogenesis. Here we review the roles of lncRNAs in regulating the angiogenic switch. We cover specific examples of both pro and anti-angiogenic lncRNAs and discuss their potential use as both prognostic biomarkers and targets for the development of future therapies.

Functional Stratification of Cancer Drugs through Integrated Network Similarity

Heterogeneity across tumors is the main obstacle in developing treatment strategies. Drug molecules not only perturb their immediate protein targets but also modulate multiple signaling pathways. In this study, we explored the networks modulated by several drug molecules across multiple cancer cell lines by integrating the drug targets with transcriptomic and phosphoproteomic data. As a result, we obtained 236 reconstructed networks covering five cell lines and 70 drugs. A rigorous topological and pathway analysis showed that chemically and functionally different drugs may modulate overlapping networks. Additionally, we revealed a set of tumor-specific hidden pathways with the help of drug network models that are not detectable from the initial data. The difference in the target selectivity of the drugs leads to disjoint networks despite sharing the exact mechanism of action, e.g., HDAC inhibitors. We also used the reconstructed network models to study potential drug combinations based on the topological separation, found literature evidence for a set of drug pairs. Overall, the network-level exploration of the drug perturbations may potentially help optimize treatment strategies and suggest new drug combinations.

An RNA Interference Tool to Silence Genes in Sarcoptes scabiei Eggs

In a quest for new interventions against scabies—a highly significant skin disease of mammals, caused by a parasitic mite Sarcoptes scabiei—we are focusing on finding new intervention targets. RNA interference (RNAi) could be an efficient functional genomics approach to identify such targets. The RNAi pathway is present in S. scabiei and operational in the female adult mite, but other developmental stages have not been assessed. Identifying potential intervention targets in the egg stage is particularly important because current treatments do not kill this latter stage. Here, we established an RNAi tool to silence single-copy genes in S. scabiei eggs. Using sodium hypochlorite pre-treatment, we succeeded in rendering the eggshell permeable to dsRNA without affecting larval hatching. We optimised the treatment of eggs with gene-specific dsRNAs to three single-copy target genes (designated Ss-Cof, Ss-Ddp, and Ss-Nan) which significantly and repeatedly suppressed transcription by ~66.6%, 74.3%, and 84.1%, respectively. Although no phenotypic alterations were detected in dsRNA-treated eggs for Ss-Cof and Ss-Nan, the silencing of Ss-Ddp resulted in a 38% reduction of larval hatching. This RNAi method is expected to provide a useful tool for larger-scale functional genomic investigations for the identification of essential genes as potential drug targets.

Prediction and analysis of functional RNA structures within the integrative genomics viewer

ABSTRACT In recent years, interest in RNA secondary structure has exploded due to its implications in almost all biological functions and its newly appreciated capacity as a therapeutic agent/target. This surge of interest has driven the development and adaptation of many computational and biochemical methods to discover novel, functional structures across the genome/transcriptome. To further enhance efforts to study RNA secondary structure, we have integrated the functional secondary structure prediction tool ScanFold, into IGV. This allows users to directly perform structure predictions and visualize results—in conjunction with probing data and other annotations—in one program. We illustrate the utility of this new tool by mapping the secondary structural landscape of the human MYC precursor mRNA. We leverage the power of vast ‘omics’ resources by comparing individually predicted structures with published data including: biochemical structure probing, RNA binding proteins, microRNA binding sites, RNA modifications, single nucleotide polymorphisms, and others that allow functional inferences to be made and aid in the discovery of potential drug targets. This new tool offers the RNA community an easy to use tool to find, analyze, and characterize RNA secondary structures in the context of all available data, in order to find those worthy of further analyses.

Mathematical Models for Cholesterol Metabolism and Transport

Cholesterol is an essential component of eukaryotic cellular membranes. It is also an important precursor for making other molecules needed by the body. Cholesterol homeostasis plays an essential role in human health. Having high cholesterol can increase the chances of getting heart disease. As a result of the risks associated with high cholesterol, it is imperative that studies are conducted to determine the best course of action to reduce whole body cholesterol levels. Mathematical models can provide direction on this. By examining existing models, the suitable reactions or processes for drug targeting to lower whole-body cholesterol can be determined. This paper examines existing models in the literature that, in total, cover most of the processes involving cholesterol metabolism and transport, including: the absorption of cholesterol in the intestine; the cholesterol biosynthesis in the liver; the storage and transport of cholesterol between the intestine, the liver, blood vessels, and peripheral cells. The findings presented in these models will be discussed for potential combination to form a comprehensive model of cholesterol within the entire body, which is then taken as an in-silico patient for identifying drug targets, screening drugs, and designing intervention strategies to regulate cholesterol levels in the human body.

Fine mapping with epigenetic information and 3D structure

Since 2005, thousands of genome-wide association studies (GWAS) have been published, identifying hundreds of thousands of genetic variants that increase risk of complex traits such as autoimmune diseases. This wealth of data has the potential to improve patient care, through personalized medicine and the identification of novel drug targets. However, the potential of GWAS for clinical translation has not been fully achieved yet, due to the fact that the functional interpretation of risk variants and the identification of causal variants and genes are challenging. The past decade has seen the development of great advances that are facilitating the overcoming of these limitations, by utilizing a plethora of genomics and epigenomics tools to map and characterize regulatory elements and chromatin interactions, which can be used to fine map GWAS loci, and advance our understanding of the biological mechanisms that cause disease.

Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination

Background LINCS, "Library of Integrated Network-based Cellular Signatures", and IDG, "Illuminating the Druggable Genome", are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson's disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches. Results Integrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG’s resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG's resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD. Conclusions The synergy of LINCS and IDG, via KG methods, empowers graph analytics methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper.

Comparative Genomics Reveals Insights into the Divergent Evolution of Astigmatic Mites and Household Pest Adaptations

Highly diversified astigmatic mites comprise many medically important human household pests such as house dust mites causing roughly 1–2% of the allergic diseases globally; however, their evolutionary origin, diverse lifestyles including reversible parasitism and quick adaptation to rather new human household environments have not been illustrated at genomic level, which hamper the allergy prevention and our exploration of these household pests. Using six high-quality assembled and annotated genomes, this comparative genomics study not only refuted the monophyly of mites and ticks, but also thoroughly explored the divergence of Acariformes and the divergent evolution of astigmatic mites. In the monophyletic Acariformes, Prostigmata known as notorious plant pests first evolved, then rapidly evolving Astigmata diverged from soil oribatid mites. Within astigmatic mites, a wide range of gene families rapidly expanded via tandem gene duplications, including ionotropic glutamate receptors, triacylglycerol lipases, serine proteases and UDP glucuronosyltransferases (UGTs), which enriched their capacities of adapting to rapidly changing household environments. The gene diversification after tandem duplications provided plenty of genetic resources for their adaptations of sensing environmental signals, digestion, and detoxification. Whilst many gene decay events only occurred in the skin-burrowing parasitic mite Sarcoptes scabiei. Throughout the evolution of Acariformes, massive horizontal gene transfer events occurred in gene families such as UGTs and several important fungal cell wall lytic enzymes, which enable the detoxification and associated digestive functions and provide perfect drug targets for pest control. Our comparative study sheds light on the rapid divergent evolution of astigmatic mites from the divergence of Acariformes to their diversification and provides novel insights into the genetic adaptations and even control of human household pests.