Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence.

Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor

Antimalarial drug design: targeting the plasmodium falciparum cytochrome bc1 complex through computational modelling, chemical synthesis and biological testing

Malaria is a life-threatening disease which is responsible for roughly one million deaths annually. Previous successes in attempting to eradicate the disease have only been short lived, owing to the increased development of resistance in the parasite. There is a continued need for novel compounds which act at novel therapeutic targets, with the Plasmodium falciparum cytochrome bc1 complex (Pfbc1) representing one such target. Its inhibition halts the biochemical generation of ATP, thus resulting in parasite cell death. Work described in this thesis was concerned with utilising molecular modelling, synthesis and biological testing to develop novel antimalarial compounds, which selectively inhibit this target. The structural details of a number of compounds known to be active or inactive against Pfbc1 were used in combination with six different ligand based virtual screening techniques, and applied to the ZINC lead like library of compounds to identify potential chemotypes active against malaria. These methods included fingerprint similarity searching, principal component analysis, and naïve Bayesian classification. The hits from each of these methods were merged and formed part of a consensus analysis in which compounds identified across several methods were deemed of more interest than those which appeared less frequently. Each molecule was given a score based on its occurrence in the virtual screening methods and also its physicochemical properties. Compounds were filtered to remove those with unfavourable chemical properties, or which contained known toxicophores. 19 compounds were ultimately purchased and tested in vitro against the 3D7 strain of the malaria parasite. 5 of the compounds reported single digit µM IC50 values, with each containing novel structural chemotypes. The lead candidate contained a benzothiazole core, and reported an IC50 value against 3D7 of 4.53 ± 1.86 µM. Additional testing showed the compounds to be inactive against bovine bc1, which is promising as strong bovine bc1 inhibition has been shown to be indicative of cardiotoxicity in humans. Molecular docking was extensively employed to rationalise the activity of Pfbc1 inhibitors such as atovaquone and HDQ. A number of quinolone containing compounds were also subject to docking, with key observations made with regard to interactions thought to be crucial to their antimalarial activity. The hits from LBVS were also the focus of docking, further supporting their potential as Pfbc1 inhibitors. QSARs were developed for a series of 4-aminoquinoline compounds which had been tested against both the NF54 and K1 strains of malaria. MLR, PLS and kNN machine learning methods were investigated, with molecular descriptors contained within valid models interpreted. Significant models were identified and shown to have strong predictive abilities for both strains. QSAR models were similarly developed for a series of thiazolide compounds with activity against hepatitis C. SVM was found to give a significant model which was able to predict the cell safety of the thiazolide derivatives. The rational design of the novel pyrroloquinolone chemotype led to the synthesis of 7 synthetic analogues to investigate its SAR, via alkylation and Winterfeldt oxidation reactions. The compounds reported 3D7 activity values between 75 nM and 1.02 µM, with molecular docking supporting their potential for Qo binding and thus Pfbc1 inhibition

Machine Learning Methods for Modeling Synthesizable Molecules

The search for new molecules often involves cycles of design-make-test-analyze steps, where new molecules are designed, synthesized in a lab, tested, and then analyzed to inform what is to be designed next. This thesis proposes new machine learning (ML) methods to augment chemists in the design and make steps of this process, focusing on the tasks of (a) how to use ML to predict chemical reaction outcomes, and (b) how to build generative models to search for new molecules. We take a common approach to both tasks, building our ML models around existing powerful tools and abstractions from the field of chemistry, and in doing so, show that the tasks we tackle are intrinsically linked. Reaction prediction is important for validating synthesis plans before carrying them out. Many previous ML approaches to reaction prediction have treated reactions as either a black box translation or a single graph edit operation. Instead, we propose a model (ELECTRO) that predicts the reaction products through modeling a sequence of electron movements. We show how modeling electron movements in this way has the benefit of being easy for chemists to interpret, and also is a natural format in which to incorporate the constraints of chemistry, such as balanced atom counts before and after a reaction. We show that our model achieves excellent performance on an important subset of chemical reactions and recovers a basic knowledge of chemistry without explicit supervision. In designing new models to search for molecules with particular properties, it is important that the models describe not only what molecule to make, but also crucially how to make it. These instructions form a synthesis plan, describing how easy-to-obtain building blocks can be combined together to form more complex molecules of interest through chemical reactions. Inspired by this real-world process, we develop two machine learning approaches that incorporate reactions into the virtual generation of new molecules. We show that aligning our model with the real-world process allows us to better link up the design and make steps involved in molecule search, and permits chemists to examine the practicability of both the final molecules we suggest and their synthetic routes. Molecule search is inherently an extrapolation task, and we show that by building our methods around the inductive biases of modeling reactions, we can generalize to new chemical spaces, suggesting molecules that not only perform well, but are synthesizable too.EPSR

A Farewell to Flat Biology. Three-dimensional Cell Culture Models in Cancer Drug Target Identification and Validation

Cells of epithelial origin, e.g. from breast and prostate cancers, effectively differentiate into complex multicellular structures when cultured in three-dimensions (3D) instead of conventional two-dimensional (2D) adherent surfaces. The spectrum of different organotypic morphologies is highly dependent on the culture environment that can be either non-adherent or scaffold-based. When embedded in physiological extracellular matrices (ECMs), such as laminin-rich basement membrane extracts, normal epithelial cells differentiate into acinar spheroids reminiscent of glandular ductal structures. Transformed cancer cells, in contrast, typically fail to undergo acinar morphogenic patterns, forming poorly differentiated or invasive multicellular structures. The 3D cancer spheroids are widely accepted to better recapitulate various tumorigenic processes and drug responses. So far, however, 3D models have been employed predominantly in the Academia, whereas the pharmaceutical industry has yet to adopt a more widely and routine use. This is mainly due to poor characterisation of cell models, lack of standardised workflows and high throughput cell culture platforms, and the availability of proper readout and quantification tools. In this thesis, a complete workflow has been established entailing well-characterised 3D cell culture models for prostate cancer, a standardised 3D cell culture routine based on high-throughput-ready platform, automated image acquisition with concomitant morphometric image analysis, and data visualisation, in order to enable large-scale high-content screens. Our integrated suite of software and statistical analysis tools were optimised and validated using a comprehensive panel of prostate cancer cell lines and 3D models. The tools quantify multiple key cancer-relevant morphological features, ranging from cancer cell invasion through multicellular differentiation to growth, and detect dynamic changes both in morphology and function, such as cell death and apoptosis, in response to experimental perturbations including RNA interference and small molecule inhibitors. Our panel of cell lines included many non-transformed and most currently available classic prostate cancer cell lines, which were characterised for their morphogenetic properties in 3D laminin-rich ECM. The phenotypes and gene expression profiles were evaluated concerning their relevance for pre-clinical drug discovery, disease modelling and basic research. In addition, a spontaneous model for invasive transformation was discovered, displaying a highdegree of epithelial plasticity. This plasticity is mediated by an abundant bioactive serum lipid, lysophosphatidic acid (LPA), and its receptor LPAR1. The invasive transformation was caused by abrupt cytoskeletal rearrangement through impaired G protein alpha 12/13 and RhoA/ROCK, and mediated by upregulated adenylyl cyclase/cyclic AMP (cAMP)/protein kinase A, and Rac/ PAK pathways. The spontaneous invasion model tangibly exemplifies the biological relevance of organotypic cell culture models. Overall, this thesis work underlines the power of novel morphometric screening tools in drug discovery.Siirretty Doriast

Molecular Science for Drug Development and Biomedicine

With the avalanche of biological sequences generated in the postgenomic age, molecular science is facing an unprecedented challenge, i.e., how to timely utilize the huge amount of data to benefit human beings. Stimulated by such a challenge, a rapid development has taken place in molecular science, particularly in the areas associated with drug development and biomedicine, both experimental and theoretical. The current thematic issue was launched with the focus on the topic of “Molecular Science for Drug Development and Biomedicine”, in hopes to further stimulate more useful techniques and findings from various approaches of molecular science for drug development and biomedicine

Antioxidant and DPPH-Scavenging Activities of Compounds and Ethanolic Extract of the Leaf and Twigs of Caesalpinia bonduc L. Roxb.

Antioxidant effects of ethanolic extract of Caesalpinia bonduc and its isolated bioactive compounds were evaluated in vitro. The compounds included two new cassanediterpenes, 1α,7α-diacetoxy-5α,6β-dihydroxyl-cass-14(15)-epoxy-16,12-olide (1)and 12α-ethoxyl-1α,14β-diacetoxy-2α,5α-dihydroxyl cass-13(15)-en-16,12-olide(2); and others, bonducellin (3), 7,4’-dihydroxy-3,11-dehydrohomoisoflavanone (4), daucosterol (5), luteolin (6), quercetin-3-methyl ether (7) and kaempferol-3-O-α-L-rhamnopyranosyl-(1Ç2)-β-D-xylopyranoside (8). The antioxidant properties of the extract and compounds were assessed by the measurement of the total phenolic content, ascorbic acid content, total antioxidant capacity and 1-1-diphenyl-2-picryl hydrazyl (DPPH) and hydrogen peroxide radicals scavenging activities.Compounds 3, 6, 7 and ethanolic extract had DPPH scavenging activities with IC50 values of 186, 75, 17 and 102 μg/ml respectively when compared to vitamin C with 15 μg/ml. On the other hand, no significant results were obtained for hydrogen peroxide radical. In addition, compound 7 has the highest phenolic content of 0.81±0.01 mg/ml of gallic acid equivalent while compound 8 showed the highest total antioxidant capacity with 254.31±3.54 and 199.82±2.78 μg/ml gallic and ascorbic acid equivalent respectively. Compound 4 and ethanolic extract showed a high ascorbic acid content of 2.26±0.01 and 6.78±0.03 mg/ml respectively.The results obtained showed the antioxidant activity of the ethanolic extract of C. bonduc and deduced that this activity was mediated by its isolated bioactive compounds

Metabolic engineering for production of triterpenoid saponin building blocks in plants and yeast

Plants are a rich source of natural products with diverse structures and biological activities beneficial to humans. This has led to their application as flavors and fragrances, colorants and therapeutics. Terpenoids or isoprenoids constitute the largest class of plant natural products with over 25,000 characterized structures. This dissertation focuses on the building blocks of triterpenoid saponins that structurally consist of 30 carbon atoms arranged in four or five ring structures with several attached oxygens and sugar moieties. The triterpenoids have been valued in traditional herbal medicine for their healing power and general health promoting activities. Despite their potential therapeutic applications, triterpenoids have not been widely employed in modern medicine, owing to the usually very low amounts in which they are produced in their natural source, the plants themselves. Engineering plants and cell cultures for the enhanced production of triterpenoids has been limited. Therefore, first and foremost a heterologous microbial Saccharomyces cerevisiae based yeast platform was established for the production of high amounts of the triterpene saponin building blocks, resulting in 36.2 mg/L β-amyrin and 46.3 mg/L lupeol producing yeast strains. Besides the establishment of a production host, the yeast strains also facilitated the identification and characterization of novel genes encoding enzymes catalyzing saponin biosynthesis from the medicinal plants Bupleurum falcatum and Maesa lanceolata, and the establishment of a combinatorial biosynthetic platform in yeast to generate novel triterpene sapogenins. Furthermore, to increase the productivity and simplify the purification pipeline of triterpene sapogenins, a methyl β-cyclodextrin based culturing approach was established to promote their release from yeast cells into the growth medium. Finally, by down regulating the expression of a Medicago truncatula saponin regulatory gene that targets specific isoforms of 3-hydroxy-3-methylglutaryl-CoA reductase, the rate controlling enzyme of the mevalonate pathway, monoglycosylated saponin building blocks could be accumulated in transgenic hairy roots, thereby serving as a plant based production system. In conclusion, two in vivo production systems were established in the model legume M. truncatula and the conventional yeast S. cerevisiae, for the synthesis of glycosylated and unglycosylated saponin building blocks, respectively