Design of the Global Health chemical diversity library v2 for screening against infectious diseases.

There is a need for novel chemical matter for phenotypic and target-based screens to find starting points for drug discovery programmes in neglected infectious diseases and non-hormonal contraceptives that disproportionately affect Low- and Middle-Income Countries (LMICs). In some disease areas multiple screens of corporate and other libraries have been carried out, giving rise to some valuable starting points and leading to preclinical candidates. Whilst in other disease areas, little screening has been carried out. Much screening against pathogens has been conducted phenotypically as there are few robustly validated protein targets. However, many of the active compound series identified share the same molecular targets. To address the need for new chemical material, in this article we describe the design of a new library, designed for screening in drug discovery programmes for neglected infectious diseases. The compounds have been selected from the Enamine REAL (REadily AccessibLe) library, a virtual library which contains approximately 4.5 billion molecules. The molecules theoretically can be synthesized quickly using commercially available intermediates and building blocks. The vast majority of these have not been prepared before, so this is a source of novel compounds. In this paper we describe the design of a diverse library of 30,000 compounds from this collection (graphical abstract). The new library will be made available to laboratories working in neglected infectious diseases, subject to a review process. The project has been supported by the Bill & Melinda Gates Foundation and the Wellcome Trust (Wellcome).

MAIP: An Open-Source Tool to Enrich High-Throughput Screening Output and Identify Novel, Druglike Molecules with Antimalarial Activity.

Efforts to tackle malaria must continue for a disease that threatens half of the global population. Parasite resistance to current therapies requires new chemotypes that are able to demonstrate effectiveness and safety. Previously, we developed a machine-learning-based approach to predict compound antimalarial activity, which was trained on the compound collections of several organizations. The resulting prediction platform, MAIP, was made freely available to the scientific community and offers a solution to prioritize molecules of interest in virtual screening and hit-to-lead optimization. Here, we experimentally validate MAIP and demonstrate how the approach was used in combination with a robust compound selection workflow and a recently introduced innovative high-throughput screening (HTS) cascade to select and purchase compounds from a public library for subsequent experimental screening. We observed a 12-fold enrichment compared with a randomly selected set of molecules, and the eight hits we ultimately selected exhibit good potency and absorption, distribution, metabolism, and excretion (ADME) profiles.

The discovery of a novel series of compounds with single-dose efficacy against juvenile and adult Schistosoma species.

Treatment and control of schistosomiasis depends on a single drug, praziquantel, but this is not ideal for several reasons including lack of potency against the juvenile stage of the parasite, dose size, and risk of resistance. We have optimised the properties of a series of compounds we discovered through high throughput screening and have designed candidates for clinical development. The best compounds demonstrate clearance of both juvenile and adult S. mansoni worms in a mouse model of infection from a single oral dose of < 10 mg/kg. Several compounds in the series are predicted to treat schistosomiasis in humans across a range of species with a single oral dose of less than 5 mg/kg.

MAIP: a web service for predicting blood-stage malaria inhibitors.

Malaria is a disease affecting hundreds of millions of people across the world, mainly in developing countries and especially in sub-Saharan Africa. It is the cause of hundreds of thousands of deaths each year and there is an ever-present need to identify and develop effective new therapies to tackle the disease and overcome increasing drug resistance. Here, we extend a previous study in which a number of partners collaborated to develop a consensus in silico model that can be used to identify novel molecules that may have antimalarial properties. The performance of machine learning methods generally improves with the number of data points available for training. One practical challenge in building large training sets is that the data are often proprietary and cannot be straightforwardly integrated. Here, this was addressed by sharing QSAR models, each built on a private data set. We describe the development of an open-source software platform for creating such models, a comprehensive evaluation of methods to create a single consensus model and a web platform called MAIP available at https://www.ebi.ac.uk/chembl/maip/ . MAIP is freely available for the wider community to make large-scale predictions of potential malaria inhibiting compounds. This project also highlights some of the practical challenges in reproducing published computational methods and the opportunities that open-source software can offer to the community.

High Throughput Screening Identifies Novel Lead Compounds with Activity against Larval, Juvenile and Adult Schistosoma mansoni.

An estimated 600 million people are affected by the helminth disease schistosomiasis caused by parasites of the genus Schistosoma. There is currently only one drug recommended for treating schistosomiasis, praziquantel (PZQ), which is effective against adult worms but not against the juvenile stage. In an attempt to identify improved drugs for treating the disease, we have carried out high throughput screening of a number of small molecule libraries with the aim of identifying lead compounds with balanced activity against all life stages of Schistosoma. A total of almost 300,000 compounds were screened using a high throughput assay based on motility of worm larvae and image analysis of assay plates. Hits were screened against juvenile and adult worms to identify broadly active compounds and against a mammalian cell line to assess cytotoxicity. A number of compounds were identified as promising leads for further chemical optimization.

Novel Amino-pyrazole Ureas with Potent In Vitro and In Vivo Antileishmanial Activity.

Visceral leishmaniasis is a severe parasitic disease that is one of the most neglected tropical diseases. Treatment options are limited, and there is an urgent need for new therapeutic agents. Following an HTS campaign and hit optimization, a novel series of amino-pyrazole ureas has been identified with potent in vitro antileishmanial activity. Furthermore, compound 26 shows high levels of in vivo efficacy (>90%) against Leishmania infantum, thus demonstrating proof of concept for this series.