Artificial intelligence (AI) uses personified knowledge and learns from the solutions it produces to address not only specific but also complex problems. Remarkable improvements in computational power coupled with advancements in AI technology could be utilised to revolutionise the drug development process. At present, the pharmaceutical industry is facing challenges in sustaining their drug development programmes because of increased R&D costs and reduced efficiency. In this review, we discuss the major causes of attrition rates in new drug approvals, the possible ways that AI can improve the efficiency of the drug development process and collaboration of pharmaceutical industry giants with AI-powered drug discovery firms.
The cluster-based compound selection is used in the lead identification process of drug discovery and design. Many clustering methods have been used for chemical databases, but there is no clustering method that can obtain the best results under all circumstances. However, little attention has been focused on the use of combination methods for chemical structure clustering, which is known as consensus clustering. Recently, consensus clustering has been used in many areas including bioinformatics, machine learning and information theory. This process can improve the robustness, stability, consistency and novelty of clustering. For chemical databases, different consensus clustering methods have been used including the co-association matrix-based, graph-based, hypergraph-based and voting-based methods. In this paper, a weighted cumulative voting-based aggregation algorithm (W-CVAA) was developed. The MDL Drug Data Report (MDDR) benchmark chemical dataset was used in the experiments and represented by the AlogP and ECPF_4 descriptors. The results from the clustering methods were evaluated by the ability of the clustering to separate biologically active molecules in each cluster from inactive ones using different criteria, and the effectiveness of the consensus clustering was compared to that of Ward's method, which is the current standard clustering method in chemoinformatics. This study indicated that weighted voting-based consensus clustering can overcome the limitations of the existing voting-based methods and improve the effectiveness of combining multiple clusterings of chemical structures.
One of the rich sources of lead compounds is the Angiosperms. Many of these lead compounds are useful medicines naturally, whereas others have been used as the basis for synthetic agents. These are potent and effective compounds, which have been obtained from plants, including anti-cancer (cytotoxic) agents, anti-malaria (anti-protozoal) agents, and anti-bacterial agents. Today, the number of plant families that have been extensively studied is relatively very few and the vast majorities have not been studied at all. The Annonaceae is the largest family in the order Magnoliales. It includes tropical trees, bushes, and climbers, which are often used as traditional remedies in Southeast Asia. Members of the Annonaceae have the particularity to elaborate a broad spectrum of natural products that have displayed anti-bacterial, anti-fungal, and anti-protozoal effects and have been used for the treatment of medical conditions, such as skin diseases, intestinal worms, inflammation of the eyes, HIV, and cancer. These special effects and the vast range of variation in potent compounds make the Annonaceae unique from other similar families in the Magnoliales and the Angiosperms in general. This paper attempts to summarize some important information and discusses a series of hypotheses about the effects of Annonaceae compounds.
Following the explosive growth in chemical and biological data, the shift from traditional methods of drug discovery to computer-aided means has made data mining and machine learning methods integral parts of today's drug discovery process. In this paper, extreme gradient boosting (Xgboost), which is an ensemble of Classification and Regression Tree (CART) and a variant of the Gradient Boosting Machine, was investigated for the prediction of biological activity based on quantitative description of the compound's molecular structure. Seven datasets, well known in the literature were used in this paper and experimental results show that Xgboost can outperform machine learning algorithms like Random Forest (RF), Support Vector Machines (LSVM), Radial Basis Function Neural Network (RBFN) and Naïve Bayes (NB) for the prediction of biological activities. In addition to its ability to detect minority activity classes in highly imbalanced datasets, it showed remarkable performance on both high and low diversity datasets.
Proteins can interact in various ways, ranging from direct physical relationships to indirect interactions in a formation of protein-protein interaction network. Diagnosis of the protein connections is critical to identify various cellular pathways. Today constructing and analyzing the protein interaction network is being developed as a powerful approach to create network pharmacology toward detecting unknown genes and proteins associated with diseases. Discovery drug targets regarding therapeutic decisions are exciting outcomes of studying disease networks. Protein connections may be identified by experimental and recent new computational approaches. Due to difficulties in analyzing in-vivo proteins interactions, many researchers have encouraged improving computational methods to design protein interaction network. In this review, the experimental and computational approaches and also advantages and disadvantages of these methods regarding the identification of new interactions in a molecular mechanism have been reviewed. Systematic analysis of complex biological systems including network pharmacology and disease network has also been discussed in this review.
Here, we explore the use of two- and three-dimensional scaffolds of multiwalled-carbon nanotubes (MWNTs) for hepatocyte cell culture. Our objective is to study the use of these scaffolds in liver tissue engineering and drug discovery. In our experiments, primary rat hepatocytes, the parenchymal (main functional) cell type in the liver, were cultured on aligned nanogrooved MWNT sheets, MWNT yarns, or standard 2-dimensional culture conditions as a control. We find comparable cell viability between all three culture conditions but enhanced production of the hepatocyte-specific marker albumin for cells cultured on MWNTs. The basal activity of two clinically relevant cytochrome P450 enzymes, CYP1A2 and CYP3A4, are similar on all substrates, but we find enhanced induction of CYP1A2 for cells on the MWNT sheets. Our data thus supports the use of these substrates for applications including tissue engineering and enhancing liver-specific functions, as well as in in vitro model systems with enhanced predictive capability in drug discovery and development.
The Sarawak Biodiversity Centre (SBC) is a state government agency which regulates research and promotes the sustainable use of biodiversity. It has a program on documentation of traditional knowledge (TK) and is well-equipped with facilities for natural product research. SBC maintains a Natural Product Library (NPL) consisting of local plant and microbial extracts for bioprospecting. The NPL is a core discovery platform for screening of bioactive compounds by researchers through a formal agreement with clear benefit sharing obligations. SBC aims to develop partnerships with leading institutions and the industries to explore the benefits of biodiversity.
Peptide-based subunit vaccines are of great interest in modern immunotherapy as they are safe, easy to produce and well defined. However, peptide antigens produce a relatively weak immune response, and thus require the use of immunostimulants (adjuvants) for optimal efficacy. Developing a safe and effective adjuvant remains a challenge for peptide-based vaccine design. Recent advances in immunology have allowed researchers to have a better understanding of the immunological implication of related diseases, which facilitates more rational design of adjuvant systems. Understanding the molecular structure of the adjuvants allows the establishment of their structure-activity relationships which is useful for the development of next-generation adjuvants. This review summarizes the current state of adjuvants development in the field of synthetic peptide-based vaccines. The structural, chemical and biological properties of adjuvants associated with their immunomodulatory effects are discussed.
Globally, thrombosis-associated disorders are one of the main contributors to fatalities. Besides genetic influences, there are some acquired and environmental risk factors dominating thrombotic diseases. Although standard regimens have been used for a long time, many side effects still occur which can be life threatening. Therefore, natural products are good alternatives. Although the quest for antithrombotic natural products came to light only since the end of last century, in the last two decades, a considerable number of natural products showing antithrombotic activities (antiplatelet, anticoagulant and fibrinolytic) with no or minimal side effects have been reported. In this review, several natural products used as antithrombotic agents including medicinal plants, vegetables, fruits, spices and edible mushrooms which have been discovered in the last 15 years and their target sites (thrombogenic components, factors and thrombotic pathways) are described. In addition, the side effects, limitations and interactions of standard regimens with natural products are also discussed. The active compounds could serve as potential sources for future research on antithrombotic drug development. As a future direction, more advanced researches (in quest of the target cofactor or component involved in antithrombotic pathways) are warranted for the development of potential natural antithrombotic medications (alone or combined with standard regimens) to ensure maximum safety and efficacy.
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.
Peptidyl-prolyl cis/trans isomerases (PPIases) are a conserved group of enzymes that catalyse the conversion between cis and trans conformations of proline imidic peptide bonds. These enzymes play critical roles in regulatory mechanisms of cellular function and pathophysiology of disease. There are three different classes of PPIases and increasing interest in the development of specific PPIase inhibitors. Cyclosporine A, FK506, rapamycin and juglone are known PPIase inhibitors. Herein, we review recent advances in elucidating the role and regulation of the PPIase family in vascular disease. We focus on peptidyl-prolyl cis/trans isomerase NIMA-interacting 1 (Pin1), an important member of the PPIase family that plays a role in cell cycle progression, gene expression, cell signalling and cell proliferation. In addition, Pin1 may be involved in atherosclerosis. The unique role of Pin1 as a molecular switch that impacts on multiple downstream pathways necessitates the evaluation of a highly specific Pin1 inhibitor to aid in potential therapeutic drug discovery.
Endophytic fungi are symbiotically related to plants and spend most of their life cycle within them. In nature, they have a crucial role in plant micro-ecosystem. They are harnessed for their bioactive compounds to counter human health problems and diseases. Endophytic Diaporthe sp. is a widely distributed fungal genus that has garnered much interest within the scientific community. A substantial number of secondary metabolites have been detected from Diaporthe sp. inhabited in various plants. As such, this minireview highlights the potential of Diaporthe sp. as a rich source of bioactive compounds by emphasizing on their diverse chemical entities and potent biological properties. The bioactive compounds produced are of significant importance to act as new lead compounds for drug discovery and development.
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.
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