Drug discovery is a highly complicated, tedious and potentially rewarding approach associated with great risk. Pharmaceutical companies literally spend millions of dollars to produce a single successful drug. The drug discovery process also need strict compliance to the directions on manufacturing and testing of new drug standards before their release into market. All these regulations created the necessity to develop advanced approaches in drug discovery. The contributions of advanced technologies including high resolution analytical instruments, 3-D biological printing, next-generation sequencing and bioinformatics have made positive impact on drug discovery & development. Fortunately, all these advanced technologies are evolving at the right time when new issues are rising in drug development process. In the present review, we have discussed the role of genomics and advanced analytical techniques in drug discovery. Further, we have also discussed the significant advances in drug discovery as case studies.
COVID-19 caused by the novel SARS-CoV-2 has been declared a pandemic by the WHO is causing havoc across the entire world. As of May end, about 6 million people have been affected, and 367 166 have died from COVID-19. Recent studies suggest that the SARS-CoV-2 genome shares about 80% similarity with the SARS-CoV-1 while their protein RNA dependent RNA polymerase (RdRp) shares 96% sequence similarity. Remdesivir, an RdRp inhibitor, exhibited potent activity against SARS-CoV-2 in vitro. 3-Chymotrypsin like protease (also known as Mpro) and papain-like protease, have emerged as the potential therapeutic targets for drug discovery against coronaviruses owing to their crucial role in viral entry and host-cell invasion. Crystal structures of therapeutically important SARS-CoV-2 target proteins, namely, RdRp, Mpro, endoribonuclease Nsp15/NendoU and receptor binding domain of CoV-2 spike protein has been resolved, which have facilitated the structure-based design and discovery of new inhibitors. Furthermore, studies have indicated that the spike proteins of SARS-CoV-2 use the Angiotensin Converting Enzyme-2 (ACE-2) receptor for its attachment similar to SARS-CoV-1, which is followed by priming of spike protein by Transmembrane protease serine 2 (TMPRSS2) which can be targeted by a proven inhibitor of TMPRSS2, camostat. The current treatment strategy includes repurposing of existing drugs that were found to be effective against other RNA viruses like SARS, MERS, and Ebola. This review presents a critical analysis of druggable targets of SARS CoV-2, new drug discovery, development, and treatment opportunities for COVID-19.
The extensive development in the strains of resistant bacteria is a potential hazard to public health worldwide. This necessitates the development of newer agents with the antibacterial property having new mechanisms of action. Mur enzymes catalyze the steps related to the biosynthesis of peptidoglycan, which constitutes a major part of the cell wall in bacteria. Peptidoglycan increases the stiffness of the cell wall, helping it to survive in unfavorable conditions. Therefore, the inhibition of Mur enzymes may lead to novel antibacterial agents that may help in controlling or overcoming bacterial resistance. Mur enzymes are classified into MurA, MurB, MurC, MurD, MurE, and MurF. Until-date, multiple inhibitors are reported for each class of the Mur enzymes. In this review, we have summarized the development of Mur enzyme inhibitors as antibacterial agents in the last few decades.
The pressing need for SARS-CoV-2 controls has led to a reassessment of strategies to identify and develop natural product inhibitors of zoonotic, highly virulent, and rapidly emerging viruses. This review article addresses how contemporary approaches involving computational chemistry, natural product (NP) and protein databases, and mass spectrometry (MS) derived target-ligand interaction analysis can be utilized to expedite the interrogation of NP structures while minimizing the time and expense of extraction, purification, and screening in BioSafety Laboratories (BSL)3 laboratories. The unparalleled structural diversity and complexity of NPs is an extraordinary resource for the discovery and development of broad-spectrum inhibitors of viral genera, including Betacoronavirus, which contains MERS, SARS, SARS-CoV-2, and the common cold. There are two key technological advances that have created unique opportunities for the identification of NP prototypes with greater efficiency: (1) the application of structural databases for NPs and target proteins and (2) the application of modern MS techniques to assess protein-ligand interactions directly from NP extracts. These approaches, developed over years, now allow for the identification and isolation of unique antiviral ligands without the immediate need for BSL3 facilities. Overall, the goal is to improve the success rate of NP-based screening by focusing resources on source materials with a higher likelihood of success, while simultaneously providing opportunities for the discovery of novel ligands to selectively target proteins involved in viral infection.
Carvacrol, called CA, is a dynamic phytoconstituent characterized by a phenol ring abundantly sourced from various natural reservoirs. This versatile scaffold serves as a pivotal template for the design and synthesis of novel drug molecules, harboring promising biological activities. The active sites positioned at C-4, C-6, and the hydroxyl group (-OH) of CA offer fertile ground for creating potent drug candidates from a pharmacological standpoint. In this comprehensive review, we delve into diverse synthesis pathways and explore the biological activity of CA derivatives. We aim to illuminate the potential of these derivatives in discovering and developing efficacious treatments against a myriad of life-threatening diseases. By scrutinizing the structural modifications and pharmacophore placements that enhance the activity of CA derivatives, we aspire to inspire the innovation of novel therapeutics with heightened potency and effectiveness.
For centuries, mushrooms have been used as food and medicine in different cultures. More recently, many bioactive compounds have been isolated from different types of mushrooms. Among these, immunomodulators have gained much interest based on the increasing growth of the immunotherapy sector. Mushroom immunomodulators are classified under four categories based on their chemical nature as: lectins, terpenoids, proteins, and polysaccharides. These compounds are produced naturally in mushrooms cultivated in greenhouses. For effective industrial production, cultivation is carried out in submerged culture to increase the bioactive compound yield, decrease the production time, and reduce the cost of downstream processing. This review provides a comprehensive overview on mushroom immunomodulators in terms of chemistry, industrial production, and applications in medical and nonmedical sectors.
There have been intense research interests in sirtuins since the establishment of their regulatory roles in a myriad of pathological processes. In the last two decades, much research efforts have been dedicated to the development of sirtuin modulators. Although synthetic sirtuin modulators are the focus, natural modulators remain an integral part to be further explored in this area as they are found to possess therapeutic potential in various diseases including cancers, neurodegenerative diseases, and metabolic disorders. Owing to the importance of this cluster of compounds, this review gives a current stand on the naturally occurring sirtuin modulators, , associated molecular mechanisms and their therapeutic benefits.. Furthermore, comprehensive data mining resulted in detailed statistical data analyses pertaining to the development trend of sirtuin modulators from 2010-2020. Lastly, the challenges and future prospect of natural sirtuin modulators in drug discovery will also be discussed.
Recent breakthroughs in G protein-coupled receptor (GPCR) crystallography and the subsequent increase in number of solved GPCR structures has allowed for the unprecedented opportunity to utilize their experimental structures for structure-based drug discovery applications. As virtual screening represents one of the primary computational methods used for the discovery of novel leads, the GPCR-Bench dataset was created to facilitate comparison among various virtual screening protocols. In this study, we have benchmarked the performance of Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) in improving virtual screening enrichment in comparison to docking with Glide, using the entire GPCR-Bench dataset of 24 GPCR targets and 254,646 actives and decoys. Reranking the top 10% of the docked dataset using MM/PBSA resulted in improvements for six targets at EF1% and nine targets at EF5%, with the gains in enrichment being more pronounced at the EF1% level. We additionally assessed the utility of rescoring the top ten poses from docking and the ability of short MD simulations to refine the binding poses prior to MM/PBSA calculations. There was no clear trend of the benefit observed in both cases, suggesting that utilizing a single energy minimized structure for MM/PBSA calculations may be the most computationally efficient approach in virtual screening. Overall, the performance of MM/PBSA rescoring in improving virtual screening enrichment obtained from docking of the GPCR-Bench dataset was found to be relatively modest and target-specific, highlighting the need for validation of MM/PBSA-based protocols prior to prospective use.
Over the years, the attention of researchers in the field of modern drug discovery and development has become further intense on the identification of active compounds from plant sources and traditional remedies, as they exhibit higher therapeutic efficacies and improved toxicological profiles. Among the large diversity of plant extracts that have been discovered and explored for their potential therapeutic benefits, asperuloside, an iridoid glycoside, has been proven to provide promising effects as a therapeutic agent for several diseases. Although, this potent substance exists in several genera, it is primarily found in plants belonging to the genus Eucommia. Recent decades have seen a surge in the research on Asperuloside, making it one of the most studied natural products in the field of medicine and pharmacology. In this review, we have attempted to study the various reported mechanisms of asperuloside that form the basis of its wide spectrum of pharmacological activities.
In vitro and in silico models of drug metabolism are utilized regularly in the drug research and development as tools for assessing pharmacokinetic variability and drug-drug interaction risk. The use of in vitro and in silico predictive approaches offers advantages including guiding rational design of clinical drug-drug interaction studies, minimization of human risk in the clinical trials, as well as cost and time savings due to lesser attrition during compound development process. This article gives a review of some of the current in vitro and in silico methods used to characterize cytochrome P450(CYP)-mediated drug metabolism for estimating pharmacokinetic variability and the magnitude of drug-drug interactions. Examples demonstrating the predictive applicability of specific in vitro and in silico approaches are described. Commonly encountered confounding factors and sources of bias and error in these approaches are presented. With the advent of technological advancement in high throughput screening and computer power, the in vitro and in silico methods are becoming more efficient and reliable and will continue to contribute to the process of drug discovery, development and ultimately safer and more effective pharmacotherapy. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
Matched MeSH terms: Drug Discovery/economics; Drug Discovery/methods*
Acanthamoeba spp. are protist pathogens and causative agents of serious infections including keratitis and granulomatous amoebic encephalitis. Its ability to convert into dormant and highly resistant cysts form limits effectiveness of available therapeutic agents and presents a pivotal challenge for drug development. During the cyst stage, Acanthamoeba is protected by the presence of hardy cyst walls, comprised primarily of carbohydrates and cyst-specific proteins, hence synthesis inhibition and/or degradation of cyst walls is of major interest. This review focuses on targeting of Acanthamoeba cysts by identifying viable therapeutic targets.
Matched MeSH terms: Drug Discovery/methods*; Drug Discovery/trends
Developing new treatments for emerging infectious diseases in infectious and noninfectious diseases has attracted a particular attention. The emergence of viral diseases is expected to accelerate; these data indicate the need for a proactive approach to develop widely active family specific and cross family therapies for future disease outbreaks. Viral disease such as pneumonia, severe acute respiratory syndrome type 2, HIV infection, and Hepatitis-C virus can cause directly and indirectly cardiovascular disease (CVD). Emphasis should be placed not only on the development of broad-spectrum molecules and antibodies but also on host factor therapy, including the reutilization of previously approved or developing drugs. Another new class of therapeutics with great antiviral therapeutic potential is molecular communication networks using deep learning autoencoder (DL-AEs). The use of DL-AEs for diagnosis and prognosis prediction of infectious and noninfectious diseases has attracted a particular attention. MCN is map to molecular signaling and communication that are found inside and outside the human body where the goal is to develop a new black box mechanism that can serve the future robust healthcare industry (HCI). MCN has the ability to characterize the signaling process between cells and infectious disease locations at various levels of the human body called point-to-point MCN through DL-AE and provide targeted drug delivery (TDD) environment. Through MCN, and DL-AE healthcare provider can remotely measure biological signals and control certain processes in the required organism for the maintenance of the patient's health state. We use biomicrodevices to promote the real-time monitoring of human health and storage of the gathered data in the cloud. In this paper, we use the DL-based AE approach to design and implement a new drug source and target for the MCN under white Gaussian noise. Simulation results show that transceiver executions for a given medium model that reduces the bit error rate which can be learned. Then, next development of molecular diagnosis such as heart sounds is classified. Furthermore, biohealth interface for the inside and outside human body mechanism is presented, comparative perspective with up-to-date current situation about MCN.
Matched MeSH terms: Drug Discovery/methods; Drug Discovery/statistics & numerical data
Spinal muscular atrophy (SMA), one of the leading inherited causes of child mortality, is a rare neuromuscular disease arising from loss-of-function mutations of the survival motor neuron 1 (SMN1) gene, which encodes the SMN protein. When lacking the SMN protein in neurons, patients suffer from muscle weakness and atrophy, and in the severe cases, respiratory failure and death. Several therapeutic approaches show promise with human testing and three medications have been approved by the U.S. Food and Drug Administration (FDA) to date. Despite the shown promise of these approved therapies, there are some crucial limitations, one of the most important being the cost. The FDA-approved drugs are high-priced and are shortlisted among the most expensive treatments in the world. The price is still far beyond affordable and may serve as a burden for patients. The blooming of the biomedical data and advancement of computational approaches have opened new possibilities for SMA therapeutic development. This article highlights the present status of computationally aided approaches, including in silico drug repurposing, network driven drug discovery as well as artificial intelligence (AI)-assisted drug discovery, and discusses the future prospects.
Matched MeSH terms: Drug Discovery/methods; Drug Discovery/trends
Many methods have been developed to capture the biological similarity between two compounds for use in drug discovery. A variety of similarity metrics have been introduced, the Tanimoto coefficient being the most prominent. Many of the approaches assume that molecular features or descriptors that do not relate to the biological activity carry the same weight as the important aspects in terms of biological similarity. Herein, a novel similarity searching approach using a Bayesian inference network is discussed. Similarity searching is regarded as an inference or evidential reasoning process in which the probability that a given compound has biological similarity with the query is estimated and used as evidence. Our experiments demonstrate that the similarity approach based on Bayesian inference networks is likely to outperform the Tanimoto similarity search and offer a promising alternative to existing similarity search approaches.
Knowledge of species richness and distribution is decisive for the composition of conservation areas. Plants typically contain many bioactive compounds are used for medicinal purposes for several disease treatment. This study aimed to identify the plant species distribution in area of UiTM Kuala Pilah, providing research scientific data and to contribute to knowledge of the use of the plants as therapeutic resources. Three quadrat frames (1x1 m), which was labeled as Set 1, 2 and 3 was developed, in each set consists of 4 plots (A, B, C and D). Characteristics of plant species were recorded, identified and classified into their respective groups. Our findings show that the most representative classes were Magnoliopsida with the total value of 71.43%, followed by Liliopsida (17.86%) and Lecanoromycetes (10.71%). A total of 28 plant species belonging to 18 families were identified in all sets with the largest family of Rubiaceae. The most distribution species are Desmodium triflorum, Dactyloctenium aegyptium, Flavoparmelia caperata, Xanthoria elegans and Phlyctis argena. Most of the plant possesses their potential to treat skin diseases, fever, ulcers and diabetes as well as digestive problems with their antimicrobial, anti-inflammatory and antioxidant properties. This study suggests that study site and plant species can be delineated as an important area to preserve these therapeutic resources. Finally, this study could also be useful for preliminary screening of potential therapeutic plant found in the study area and useful for the researchers in the pursuit of novel drug discovery.
Medicinal plants are the main natural pools for the discovery and development of new drugs. In the modern era of computer-aided drug designing (CADD), there is need of prompt efforts to design and construct useful database management system that allows proper data storage, retrieval and management with user-friendly interface. An inclusive database having information about classification, activity and ready-to-dock library of medicinal plant's phytochemicals is therefore required to assist the researchers in the field of CADD. The present work was designed to merge activities of phytochemicals from medicinal plants, their targets and literature references into a single comprehensive database named as Medicinal Plants Database for Drug Designing (MPD3). The newly designed online and downloadable MPD3 contains information about more than 5000 phytochemicals from around 1000 medicinal plants with 80 different activities, more than 900 literature references and 200 plus targets. The designed database is deemed to be very useful for the researchers who are engaged in medicinal plants research, CADD and drug discovery/development with ease of operation and increased efficiency. The designed MPD3 is a comprehensive database which provides most of the information related to the medicinal plants at a single platform. MPD3 is freely available at: http://bioinform.info .
There have been significant achievements in research at IMU as indicated by the increasing amount of external funds obtained, and number of publications and postgraduate students produced since it started its research activities in the year 2000. However, it is a great challenge indeed to ensure sustainability of our research, which is currently heavily dependent on internal funding. There is a need to realign our strategies to further enhance our competitiveness in securing external funding for research. In line with this, the Institute for Research, Development and Innovation (IRDI) was officially established on 18 September 2012. The Institute will serve as a platform to support all research activities at IMU. There are four Centres of Excellence based on the identified thrust areas under
IRDI, namely 1) Centre for Bioactive Molecules and Drug Discovery; 2) Centre for Environmental and
Population Health; 3) Centre for Cancer and Stem Cell Research, and 4) Centre for Health Professional Education Research. Major findings based on research in these four thrust areas are reviewed in this paper. With the strategic planning and establishment of IRDI, it is our aspiration to bring research at IMU to a higher level.
Nucleobases represent key structural motifs in biologically active molecules, including synthetic and natural products. Molecular modifications made on nucleobases or their isolation from natural sources are being widely investigated for the development of drugs with improved potency for the treatment of different diseases, such as cancer, as well as viral and bacterial infections. This review article focuses on the nucleobase analogue drug developments of the past 20 years (2000-2020). Various pharmacological and medicinal aspects of nucleobases and their analogues are discussed. The current state and limitations are also highlighted.
The skin is the largest organ in the human body, composed of the epidermis and the dermis. It provides protection and acts as a barrier against external menaces like allergens, chemicals, systemic toxicity, and infectious organisms. Skin disorders like cancer, dermatitis, psoriasis, wounds, skin aging, acne, and skin infection occur frequently and can impact human life. According to a growing body of evidence, several studies have reported that natural products have the potential for treating skin disorders. Building on this information, this review provides brief information about the action of the most important in vitro and in vivo research on the use of ten selected natural products in inflammatory, neoplastic, and infectious skin disorders and their mechanisms that have been reported to date. The related studies and articles were searched from several databases, including PubMed, Google, Google Scholar, and ScienceDirect. Ten natural products that have been reported widely on skin disorders were reviewed in this study, with most showing anti-inflammatory, antioxidant, anti-microbial, and anti-cancer effects as the main therapeutic actions. Overall, most of the natural products reported in this review can reduce and suppress inflammatory markers, like tumor necrosis factor-alpha (TNF-α), scavenge reactive oxygen species (ROS), induce cancer cell death through apoptosis, and prevent bacteria, fungal, and virus infections indicating their potentials. This review also highlighted the challenges and opportunities of natural products in transdermal/topical delivery systems and their safety considerations for skin disorders. Our findings indicated that natural products might be a low-cost, well-tolerated, and safe treatment for skin diseases. However, a larger number of clinical trials are required to validate these findings. Natural products in combination with modern drugs, as well as the development of novel delivery mechanisms, represent a very promising area for future drug discovery of these natural leads against skin disorders.
Computational target fishing plays an important role in target identification, particularly in drug discovery campaigns utilizing phenotypic screening. Numerous approaches exist to predict potential targets for a given ligand, but true targets may be inconsistently ranked. More advanced simulation methods may provide benefit in such cases by reranking these initial predictions. We evaluated the ability of binding pose metadynamics to improve the predicted rankings for three diverse ligands and their six true targets. Initial predictions using pharmacophore mapping showed no true targets ranked in the top 50 and two targets each ranked within the 50-100, 100-150, and 250-300 ranges respectively. Following binding pose metadynamics, ranking of true targets improved for four out of the six targets and included the highest ranked predictions overall, while rankings deteriorated for two targets. The revised rankings predicted two true targets ranked within the top 50, and one target each within the 50-100, 100-150, 150-200, and 200-250 ranges respectively. The findings of this study demonstrate that binding pose metadynamics may be of benefit in refining initial predictions from structure-based target fishing algorithms, thereby improving the efficiency of the target identification process in drug discovery efforts.