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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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*
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.
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.
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 .
Benzimidazole is a valuable pharmacophore in the field of medicinal chemistry and exhibit wide spectrum of biological activity. Molecular docking technique is routinely used in modern drug discovery for understanding the drug-receptor interaction. The selected data set of synthesized benzimidazole compounds was evaluated for its in vitro anticancer activity against cancer cell lines (HCT116 and MCF7) by sulforhodamine B (SRB) assay. Further, molecular docking study of data set was carried out by Schrodinger-Maestro v11.5 using CDK-8 (PDB code: 5FGK) and ER-alpha (PDB code: 3ERT) as possible target for anticancer activity. Molecular docking results demonstrated that compounds 12, 16, N9, W20 and Z24 displayed good docking score with better interaction within crucial amino acids and corelate to their anticancer results. ADME results indicated that compounds 16, N9 and W20 have significant results within the close agreement of the Lipinski's rule of five and Qikprop rule within the range and these compounds may be taken as lead molecules for the discovery of new anticancer agents.