Modern Approaches in the Discovery and Development of Plant-Based Natural Products and Their Analogues as Potential Therapeutic Agents

Natural products represents an important source of new lead compounds in drug discovery research. Several drugs currently used as therapeutic agents have been developed from natural sources; plant sources are specifically important. In the past few decades, pharmaceutical companies demonstrated insignificant attention towards natural product drug discovery, mainly due to its intrinsic complexity. Recently, technological advancements greatly helped to address the challenges and resulted in the revived scientific interest in drug discovery from natural sources. This review provides a comprehensive overview of various approaches used in the selection, authentication, extraction/isolation, biological screening, and analogue development through the application of modern drug-development principles of plant-based natural products. Main focus is given to the bioactivity-guided fractionation approach along with associated challenges and major advancements. A brief outline of historical development in natural product drug discovery and a snapshot of the prominent natural drugs developed in the last few decades are also presented. The researcher’s opinions indicated that an integrated interdisciplinary approach utilizing technological advances is necessary for the successful development of natural products. These involve the application of efficient selection method, well-designed extraction/isolation procedure, advanced structure elucidation techniques, and bioassays with a high-throughput capacity to establish druggability and patentability of phyto-compounds. A number of modern approaches including molecular modeling, virtual screening, natural product library, and database mining are being used for improving natural product drug discovery research. Renewed scientific interest and recent research trends in natural product drug discovery clearly indicated that natural products will play important role in the future development of new therapeutic drugs and it is also anticipated that efficient application of new approaches will further improve the drug discovery campaign.

Potential Pharmaceutical Application of Endemic Plants: When will Sri Lanka Understand the Economic Value Chain?

Small molecules has been a main concern in the pharmaceutical industry for as long as they have existed. Enormous libraries of compounds have been collected and they in turn nurture drug discovery research. For example, big pharma, has in their compound libraries ranging from 500,000 to several million. Examining the drugs in the market, it is clear from where most are arriving: natural origin; out of the 1,328 new chemical entities approved as drugs between 1981 and 2016, only 359 were purely of synthetic origin. In the list of remaining ones, 326 were “biologics”, and 94 were vaccines. Importantly, 549 were from natural origin or arose motivated from natural compounds. Furthermore, anticancer compounds arising during the same period (1981–2014), only 23 were purely synthetic (Newman and Cragg, 2016). Natural origin can count for three categories: unaltered natural products; distinct mixture of natural products and natural product derivatives isolated from plants or other living organisms such as fungi, sponges, lichens, or microorganisms; and products modified through application of medicinal chemistry. There are many examples covering a wide spectrum of diseases: anticancer drugs such as docetaxel (Taxotere™), paclitaxel (Taxol™), vinblastine, podophyllotoxin (Condylin™), or etoposide; steroidal hormones such as progesterone, norgestrel, or cortisone; cardiac glycosides such as digitoxigenin; antibiotics like penicillin, streptomycin, and cephalosporins.

One-pot Synthesis of Pyrano[2,3-c]pyrazole Derivatives via Multicomponent Reactions (MCRs) and their Applications in Medicinal Chemistry

Background: Many pyrano[2,3-c]pyrazole derivatives display diverse biological activities and some of them are known as anticancer, analgesic, anticonvulsant, antimicrobial, anti-inflammatory, and anti-malarial agents. In recent years, easy convergent, multicomponent reactions (MCRs) have been adopted to make highly functionalizedpyrano[2,3-c]pyrazole derivatives of biological interest. The synthesis of 1,4-dihydropyrano[2,3-c]pyrazole (1,4-DHPP, 2), 2,4-dihydropyrano[2,3-c]pyrazole (2,4-DHPP, 3), 4-hydroxypyrano[2,3-c]pyrazole (4-HPP, 4) derivatives, 1,4,4-substitied pyranopyrazole (SPP, 5) were reported via two-, three-, four- and five-component reactions (MCRs). Methods: This review article compiles the preparation of pyrano[2,3-c]pyrazole derivatives, and it highlights the applications of various pyrano[2,3-c]pyrazole derivatives in medicinal chemistry. Results: Varieties of pyrano[2,3-c]pyrazole derivatives were achieved via “One-pot” multicomponent reactions (MCRs). Different reaction conditions in the presence of a catalyst or without catalysts were adapted to prepare the pyrano[2,3-c]pyrazole derivatives. Conclusion: Biologically active pyrano[2,3-c]pyrazole derivatives were prepared and used in drug discovery research.

Transferable Neural Network Potential Energy Surfaces for Closed-Shell Organic Molecules: Extension to Ions

Transferable high dimensional neural network potentials (HDNNP) have shown great promise as an avenue to increase the accuracy and domain of applicability of existing atomistic force fields for organic systems relevant to life science. We have previously reported such a potential (Schrödinger-ANI) that has broad coverage of druglike molecules. We extend that work here to cover ionic and zwitterionic druglike molecules expected to be relevant to drug discovery research activities. We report a novel HDNNP architechture, which we call QRNN, that predicts atomic charges and uses these charges as descriptors in an energy model which delivers conformational energies within chemical accuracy when measured against the reference theory it is trained to. Further, we find that delta learning based on a semi-empirical level of theory approximately halves the errors. We test the models on torsion energy profiles, relative conformational energies, geometric parameters and relative tautomer errors.

Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network

As safety is one of the most important properties of drugs, chemical toxicology prediction has received increasing attentions in the drug discovery research. Traditionally, researchers rely on in vitro and in vivo experiments to test the toxicity of chemical compounds. However, not only are these experiments time consuming and costly, but experiments that involve animal testing are increasingly subject to ethical concerns. While traditional machine learning (ML) methods have been used in the field with some success, the limited availability of annotated toxicity data is the major hurdle for further improving model performance. Inspired by the success of semi-supervised learning (SSL) algorithms, we propose a Graph Convolution Neural Network (GCN) to predict chemical toxicity and trained the network by the Mean Teacher (MT) SSL algorithm. Using the Tox21 data, our optimal SSL-GCN models for predicting the twelve toxicological endpoints achieve an average ROC-AUC score of 0.757 in the test set, which is a 6% improvement over GCN models trained by supervised learning and conventional ML methods. Our SSL-GCN models also exhibit superior performance when compared to models constructed using the built-in DeepChem ML methods. This study demonstrates that SSL can increase the prediction power of models by learning from unannotated data. The optimal unannotated to annotated data ratio ranges between 1:1 and 4:1. This study demonstrates the success of SSL in chemical toxicity prediction; the same technique is expected to be beneficial to other chemical property prediction tasks by utilizing existing large chemical databases. Our optimal model SSL-GCN is hosted on an online server accessible through: https://app.cbbio.online/ssl-gcn/home.

A low-cost open-source 5-choice operant box system optimized for electrophysiology and optophysiology in mice

Operant boxes enable the application of complex behavioural paradigms to support circuit neuroscience and drug discovery research. However, commercial operant box systems are expensive and often not optimised for combining behaviour with neurophysiology. Here we introduce a fully open-source Python-based operant-box system in a 5-choice design (pyOS-5) that enables assessment of multiple cognitive and affective functions. It is optimized for fast turn-over between animals, and for testing of tethered mice for simultaneous physiological recordings or optogenetic manipulation. For reward delivery, we developed peristaltic and syringe pumps based on a stepper motor and 3D-printed parts. Tasks are specified using a Python-based syntax implemented on custom-designed printed circuit boards that are commercially available at low cost. We developed an open-source graphical user interface (GUI) and task definition scripts to conduct assays assessing operant learning, attention, impulsivity, working memory, or cognitive flexibility, alleviating the need for programming skills of the end user. All behavioural events are recorded with millisecond resolution, and TTL-outputs and -inputs allow straightforward integration with physiological recordings and closed-loop manipulations. This combination of features realizes a cost-effective, nose-poke-based operant box system that allows reliable circuit-neuroscience experiments investigating correlates of cognition and emotion in large cohorts of subjects.

Custom ML Module of AIDrugApp for Molecular Identification, Descriptor Calculation, and Building ML/DL QSAR Models

Computer-aided drug design (CADD) techniques continue to struggle to provide a useful advance in the area of drug development due to the difficulties in an efficient exploration of the vast drug-like chemical space to uncover new chemical compounds with desired biological properties. Other challenges that users must overcome in order to fully use the potential of CADD tools and techniques include a lack of completely autonomous methods, the necessity for retraining even after deployment, and their lack of interpretability. To solve this issue, we created the ‘Custom ML Tools’ integrated within the framework of ‘AIDrugAPP’. ‘Custom ML Tools’ includes four modules: ‘Mol Identifier’, ‘DesCal’, ‘AutoDL’, and ‘Auto-Multi-ML’ which give users free access to molecular identification using SMILES and compound names, similarity search, descriptor calculation, the building of ML/DL QSAR models, and their usage in predicting new data. The study demonstrates the potential of the novel tool for computational investigations in drug discovery research. The WebApp with its modules has therefore been made available for public use at: https://sars-covid-app.herokuapp.com/

Cryo-EM structure of a single-chain β1–adrenoceptor – AmpC β-lactamase fusion protein

The insertion of fusion proteins has enabled the crystallization of a wide range of G–protein–coupled receptors (GPCRs). Here, we explored the possibility of using a larger fusion protein, inserted into the third intracellular loop (ICL3) of β1-adrenoceptor (β1AR) via rigid chimeric helix fusions. The aim was to engineer a single–chain fusion protein that comprises sufficient mass and rigidity to allow single–particle cryo–EM data collection, without depending on binding proteins, such as G–proteins or nanobodies. Through parsing of the protein data bank (PDB), we identified the protein AmpC–β–lactamase as a suitable candidate. Both termini of this protein are α–helical and the helices are antiparallel to each other. The distance between their centroids measures ≈11 Å. Such a geometry is ideal to design extended chimeric helices with transmembrane (TM) helices 5 and 6 of β1AR, and the insertion of the protein adds ≈39 kDa of mass to the receptor. We expressed the β1AR – AmpC β–lactamase fusion protein in mammalian cells. The binding of the antagonists propranolol and cyanopindolol to the purified fusion protein was confirmed by CPM–based thermofluor assays. The cryo–EM structure was solved to a nominal overall resolution of 3.6 Å and the seven helix architecture and helix eight were clearly resolved. Superimposition of the structure with known X–ray crystal structures of β1AR suggests that the protein is in its inactive conformation. The fusion protein described here provides a basis for high–throughput structure elucidation of class A GPCRs by cryo–EM for drug discovery research as well as for the elucidation of inactive state or wild–type GPCR structures. The fusion protein geometry theoretically fits a wide range of class A GPCRs and therefore can be applied to a multitude of receptors.