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Somatic mutations in ACVR1 are found in a quarter of children with diffuse intrinsic pontine glioma (DIPG), but there are no ACVR1 inhibitors licensed for the disease. Using an artificial intelligence–based platform to search for approved compounds for ACVR1-mutant DIPG, the combination of vandetanib and everolimus was identified as a possible therapeutic approach. Vandetanib, an inhibitor of VEGFR/RET/EGFR, was found to target ACVR1 (Kd = 150 nmol/L) and reduce DIPG cell viability in vitro but has limited ability to cross the blood–brain barrier. In addition to mTOR, everolimus inhibited ABCG2 (BCRP) and ABCB1 (P-gp) transporters and was synergistic in DIPG cells when combined with vandetanib in vitro. This combination was well tolerated in vivo and significantly extended survival and reduced tumor burden in an orthotopic ACVR1-mutant patient-derived DIPG xenograft model. Four patients with ACVR1-mutant DIPG were treated with vandetanib plus an mTOR inhibitor, informing the dosing and toxicity profile of this combination for future clinical studies.Significance:Twenty-five percent of patients with the incurable brainstem tumor DIPG harbor somatic activating mutations in ACVR1, but there are no approved drugs targeting the receptor. Using artificial intelligence, we identify and validate, both experimentally and clinically, the novel combination of vandetanib and everolimus in these children based on both signaling and pharmacokinetic synergies.This article is highlighted in the In This Issue feature, p. 275

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Somatic mutations in ACVR1 are found in a quarter of children with diffuse intrinsic pontine glioma (DIPG), but there are no ACVR1 inhibitors licensed for the disease. Using an artificial intelligence–based platform to search for approved compounds for ACVR1-mutant DIPG, the combination of vandetanib and everolimus was identified as a possible therapeutic approach. Vandetanib, an inhibitor of VEGFR/RET/EGFR, was found to target ACVR1 (Kd = 150 nmol/L) and reduce DIPG cell viability in vitro but has limited ability to cross the blood–brain barrier. In addition to mTOR, everolimus inhibited ABCG2 (BCRP) and ABCB1 (P-gp) transporters and was synergistic in DIPG cells when combined with vandetanib in vitro. This combination was well tolerated in vivo and significantly extended survival and reduced tumor burden in an orthotopic ACVR1-mutant patient-derived DIPG xenograft model. Four patients with ACVR1-mutant DIPG were treated with vandetanib plus an mTOR inhibitor, informing the dosing and toxicity profile of this combination for future clinical studies. Significance: Twenty-five percent of patients with the incurable brainstem tumor DIPG harbor somatic activating mutations in ACVR1, but there are no approved drugs targeting the receptor. Using artificial intelligence, we identify and validate, both experimentally and clinically, the novel combination of vandetanib and everolimus in these children based on both signaling and pharmacokinetic synergies. This article is highlighted in the In This Issue feature, p. 275

Can traditional computational analysis and machine learning help compensate for inadequate peer review of drug-repurposing papers in the context of an infodemic?

Lack of efficacy in the intended disease indication is the major cause of clinical phase drug development failure. Explanations could include the poor external validity of pre-clinical (cell, tissue, and animal) models of human disease and the high false discovery rate (FDR) in preclinical science. FDR is related to the proportion of true relationships available for discovery (γ), and the type 1 (false-positive) and type 2 (false negative) error rates of the experiments designed to uncover them. We estimated the FDR in preclinical science, its effect on drug development success rates, and improvements expected from use of human genomics rather than preclinical studies as the primary source of evidence for drug target identification. Calculations were based on a sample space defined by all human diseases – the ‘disease-ome’ – represented as columns; and all protein coding genes – ‘the protein-coding genome’– represented as rows, producing a matrix of unique gene- (or protein-) disease pairings. We parameterised the space based on 10,000 diseases, 20,000 protein-coding genes, 100 causal genes per disease and 4000 genes encoding druggable targets, examining the effect of varying the parameters and a range of underlying assumptions, on the inferences drawn. We estimated γ, defined mathematical relationships between preclinical FDR and drug development success rates, and estimated improvements in success rates based on human genomics (rather than orthodox preclinical studies). Around one in every 200 protein-disease pairings was estimated to be causal (γ = 0.005) giving an FDR in preclinical research of 92.6%, which likely makes a major contribution to the reported drug development failure rate of 96%. Observed success rate was only slightly greater than expected for a random pick from the sample space. Values for γ back-calculated from reported preclinical and clinical drug development success rates were also close to the a priori estimates. Substituting genome wide (or druggable genome wide) association studies for preclinical studies as the major information source for drug target identification was estimated to reverse the probability of late stage failure because of the more stringent type 1 error rate employed and the ability to interrogate every potential druggable target in the same experiment. Genetic studies conducted at much larger scale, with greater resolution of disease end-points, e.g. by connecting genomics and electronic health record data within healthcare systems has the potential to produce radical improvement in drug development success rate.

Following multiple warnings from governments and health organisations, there has been renewed investment, led by the public sector, in the discovery of novel antimicrobials to meet the challenge of rising levels of drug-resistant infection, particularly in the case of resistance to antibiotics. Initiatives have also been announced to support and enable the antibiotic discovery process. In January 2018, the Medicines Discovery Catapult, UK, hosted a symposium: Next Generation Antibiotics Discovery, to consider the latest initiatives and any remaining challenges to inform and guide the international research community and better focus resources to yield a novel class of antibiotic.

Somatic mutations in ACVR1, encoding the serine/threonine kinase ALK2 receptor, are found in a quarter of children with the currently incurable brain tumour diffuse intrinsic pontine glioma (DIPG). Treatment of ACVR1-mutant DIPG patient-derived models with multiple inhibitor chemotypes leads to a reduction in cell viability in vitro and extended survival in orthotopic xenografts in vivo, though there are currently no specific ACVR1 inhibitors licensed for DIPG. Using an Artificial Intelligence-based platform to search for approved compounds which could be used to treat ACVR1-mutant DIPG, the combination of vandetanib and everolimus was identified as a possible therapeutic approach. Vandetanib, an approved inhibitor of VEGFR/RET/EGFR, was found to target ACVR1 (Kd=150nM) and reduce DIPG cell viability in vitro, but has been trialed in DIPG patients with limited success, in part due to an inability to cross the blood-brain-barrier. In addition to mTOR, everolimus inhibits both ABCG2 (BCRP) and ABCB1 (P-gp) transporter, and was synergistic in DIPG cells when combined with vandetanib in vitro. This combination is well-tolerated in vivo, and significantly extended survival and reduced tumour burden in an orthotopic ACVR1-mutant patient-derived DIPG xenograft model. Based on these preclinical data, three patients with ACVR1-mutant DIPG were treated with vandetanib and everolimus. These cases may inform on the dosing and the toxicity profile of this combination for future clinical studies. This bench-to-bedside approach represents a rapidly translatable therapeutic strategy in children with ACVR1 mutant DIPG.

Somatic mutations in ACVR1 are found in a quarter of children with diffuse intrinsic pontine glioma (DIPG), however there are no ACVR1 inhibitors licensed for the disease. Using an Artificial Intelligence-based platform to search for approved compounds for ACVR1-mutant DIPG, the combination of vandetanib and everolimus was identified as a possible therapeutic approach. Vandetanib, an inhibitor of VEGFR/RET/EGFR, was found to target ACVR1 (Kd=150nM) and reduce DIPG cell viability in vitro, but has limited ability to cross the blood-brain-barrier. In addition to mTOR, everolimus inhibits ABCG2 (BCRP) and ABCB1 (P-gp) transporters, and was synergistic in DIPG cells when combined with vandetanib in vitro. This combination is well-tolerated in vivo, and significantly extended survival and reduced tumor burden in an orthotopic ACVR1-mutant patient-derived DIPG xenograft model. Four patients with ACVR1-mutant DIPG were treated with vandetanib plus mTOR inhibitor, informing the dosing and toxicity profile of this combination for future clinical studies.

In May 2019, the Wellcome Centre for Anti-Infectives Research (; W; CAIR) at the University of Dundee, UK, held an international conference with the aim of discussing some key questions around discovering new medicines for infectious diseases and a particular focus on diseases affecting Low and Middle Income Countries. There is an urgent need for new drugs to treat most infectious diseases. We were keen to see if there were lessons that we could learn across different disease areas and between the preclinical and clinical phases with the aim of exploring how we can improve and speed up the drug discovery, translational, and clinical development processes. We started with an introductory session on the current situation and then worked backward from clinical development to combination therapy, pharmacokinetic/pharmacodynamic (PK/PD) studies, drug discovery pathways, and new starting points and targets. This Viewpoint aims to capture some of the learnings.

ChEMBL is an open large-scale bioactivity database (https://www.ebi.ac.uk/chembl), previously described in the 2012 and 2014 Nucleic Acids Research Database Issues. Since then, alongside the continued extraction of data from the medicinal chemistry literature, new sources of bioactivity data have also been added to the database. These include: deposited data sets from neglected disease screening; crop protection data; drug metabolism and disposition data and bioactivity data from patents. A number of improvements and new features have also been incorporated. These include the annotation of assays and targets using ontologies, the inclusion of targets and indications for clinical candidates, addition of metabolic pathways for drugs and calculation of structural alerts. The ChEMBL data can be accessed via a web-interface, RDF distribution, data downloads and RESTful web-services.

The 'druggable genome' encompasses several protein families, but only a subset of targets within them have attracted significant research attention and thus have information about them publicly available. The Illuminating the Druggable Genome (IDG) program was initiated in 2014, has the goal of developing experimental techniques and a Knowledge Management Center (KMC) that would collect and organize information about protein targets from four families, representing the most common druggable targets with an emphasis on understudied proteins. Here, we describe two resources developed by the KMC: the Target Central Resource Database (TCRD) which collates many heterogeneous gene/protein datasets and Pharos (https://pharos.nih.gov), a multimodal web interface that presents the data from TCRD. We briefly describe the types and sources of data considered by the KMC and then highlight features of the Pharos interface designed to enable intuitive access to the IDG knowledgebase. The aim of Pharos is to encourage 'serendipitous browsing', whereby related, relevant information is made easily discoverable. We conclude by describing two use cases that highlight the utility of Pharos and TCRD.