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1.
Cancer Discov ; 12(2): 416-431, 2022 02.
Artigo em Inglês | MEDLINE | ID: mdl-34551970

RESUMO

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 (K d = 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.


Assuntos
Receptores de Ativinas Tipo I/genética , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Neoplasias do Tronco Encefálico/tratamento farmacológico , Everolimo/uso terapêutico , Glioma/tratamento farmacológico , Piperidinas/uso terapêutico , Quinazolinas/uso terapêutico , Animais , Protocolos de Quimioterapia Combinada Antineoplásica/administração & dosagem , Neoplasias do Tronco Encefálico/mortalidade , Criança , Pré-Escolar , Reposicionamento de Medicamentos , Everolimo/administração & dosagem , Feminino , Glioma/mortalidade , Humanos , Masculino , Camundongos , Camundongos SCID , Piperidinas/administração & dosagem , Quinazolinas/administração & dosagem , Ratos , Resultado do Tratamento
2.
Cochrane Database Syst Rev ; 10: CD011748, 2020 10 20.
Artigo em Inglês | MEDLINE | ID: mdl-33078867

RESUMO

BACKGROUND: Despite the availability of effective drug therapies that reduce low-density lipoprotein (LDL)-cholesterol (LDL-C), cardiovascular disease (CVD) remains an important cause of mortality and morbidity. Therefore, additional LDL-C reduction may be warranted, especially for people who are unresponsive to, or unable to take, existing LDL-C-reducing therapies. By inhibiting the proprotein convertase subtilisin/kexin type 9 (PCSK9) enzyme, monoclonal antibodies (PCSK9 inhibitors) reduce LDL-C and CVD risk. OBJECTIVES: Primary To quantify the effects of PCSK9 inhibitors on CVD, all-cause mortality, myocardial infarction, and stroke, compared to placebo or active treatment(s) for primary and secondary prevention. Secondary To quantify the safety of PCSK9 inhibitors, with specific focus on the incidence of influenza, hypertension, type 2 diabetes, and cancer, compared to placebo or active treatment(s) for primary and secondary prevention. SEARCH METHODS: We identified studies by systematically searching CENTRAL, MEDLINE, Embase, and Web of Science in December 2019. We also searched ClinicalTrials.gov and the International Clinical Trials Registry Platform in August 2020 and screened the reference lists of included studies. This is an update of the review first published in 2017. SELECTION CRITERIA: All parallel-group and factorial randomised controlled trials (RCTs) with a follow-up of at least 24 weeks were eligible. DATA COLLECTION AND ANALYSIS: Two review authors independently reviewed and extracted data. Where data were available, we calculated pooled effect estimates. We used GRADE to assess certainty of evidence and in 'Summary of findings' tables. MAIN RESULTS: We included 24 studies with data on 60,997 participants. Eighteen trials randomised participants to alirocumab and six to evolocumab. All participants received background lipid-lowering treatment or lifestyle counselling. Six alirocumab studies used  an active treatment comparison group (the remaining used placebo), compared to three evolocumab active comparison trials. Alirocumab compared with placebo decreased the risk of CVD events, with an absolute risk difference (RD) of -2% (odds ratio (OR) 0.87, 95% confidence interval (CI) 0.80 to 0.94; 10 studies, 23,868 participants; high-certainty evidence), decreased the risk of mortality (RD -1%; OR 0.83, 95% CI 0.72 to 0.96; 12 studies, 24,797 participants; high-certainty evidence), and MI (RD -2%; OR 0.86, 95% CI 0.79 to 0.94; 9 studies, 23,352 participants; high-certainty evidence) and for any stroke (RD 0%; OR 0.73, 95% CI 0.58 to 0.91; 8 studies, 22,835 participants; high-certainty evidence). Compared to active treatment the alirocumab effects, for CVD, the RD was 1% (OR 1.37, 95% CI 0.65 to 2.87; 3 studies, 1379 participants; low-certainty evidence); for mortality, RD was -1% (OR 0.51, 95% CI 0.18 to 1.40; 5 studies, 1333 participants; low-certainty evidence); for MI, RD was 1% (OR 1.45, 95% CI 0.64 to 3.28, 5 studies, 1734 participants; low-certainty evidence); and for any stroke, RD was less than 1% (OR 0.85, 95% CI 0.13 to 5.61; 5 studies, 1734 participants; low-certainty evidence). Compared to placebo the evolocumab, for CVD, the RD was -2% (OR 0.84, 95% CI 0.78 to 0.91; 3 studies, 29,432 participants; high-certainty evidence); for mortality, RD was less than 1% (OR 1.04, 95% CI 0.91 to 1.19; 3 studies, 29,432 participants; high-certainty evidence); for MI, RD was -1% (OR 0.72, 95% CI 0.64 to 0.82; 3 studies, 29,432 participants; high-certainty evidence); and for any stroke RD was less than -1% (OR 0.79, 95% CI 0.65 to 0.94; 2 studies, 28,531 participants; high-certainty evidence).  Compared to active treatment, the evolocumab effects, for any CVD event RD was less than -1% (OR 0.66, 95% CI 0.14 to 3.04; 1 study, 218 participants; very low-certainty evidence); for all-cause mortality, the RD was less than 1% (OR 0.43, 95% CI 0.14 to 1.30; 3 studies, 5223 participants; very low-certainty evidence); and for MI, RD was less than 1% (OR 0.66, 95% CI 0.23 to 1.85; 3 studies, 5003 participants; very low-certainty evidence). There were insufficient data on any stroke.  AUTHORS' CONCLUSIONS: The evidence for the clinical endpoint effects of  evolocumab and alirocumab were graded as high. There is a strong evidence base to prescribe PCSK9 monoclonal antibodies to people who might not be eligible for other lipid-lowering drugs, or to people who cannot meet their lipid goals on more traditional therapies, which was the main patient population of the available trials.  The evidence base of PCSK9 inhibitors compared with active treatment is much weaker (low very- to low-certainty evidence) and it is unclear whether evolocumab or alirocumab might be effectively used as replacement therapies. Related, most of the available studies preferentially enrolled people with either established CVD or at a high risk already, and evidence in low- to medium-risk settings is minimal. Finally, there is very limited evidence on any potential safety issues of both evolocumab and alirocumab. While the current evidence synthesis does not reveal any adverse signals, neither does it provide evidence against such signals. This suggests careful consideration of alternative lipid lowering treatments before prescribing PCSK9 inhibitors.


Assuntos
Anticorpos Monoclonais Humanizados/uso terapêutico , Doenças Cardiovasculares/prevenção & controle , LDL-Colesterol/sangue , Inibidores de PCSK9 , Anticolesterolemiantes/uso terapêutico , Causas de Morte , Antagonistas Colinérgicos/uso terapêutico , Ezetimiba/uso terapêutico , Humanos , Inibidores de Hidroximetilglutaril-CoA Redutases/uso terapêutico , Pessoa de Meia-Idade , Infarto do Miocárdio/epidemiologia , Prevenção Primária/métodos , Pró-Proteína Convertase 9/imunologia , Ensaios Clínicos Controlados Aleatórios como Assunto , Prevenção Secundária/métodos , Acidente Vascular Cerebral/epidemiologia , Fatores de Tempo
4.
ACS Infect Dis ; 6(1): 3-13, 2020 01 10.
Artigo em Inglês | MEDLINE | ID: mdl-31808676

RESUMO

In May 2019, the Wellcome Centre for Anti-Infectives Research (WCAIR) 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.


Assuntos
Controle de Doenças Transmissíveis , Doenças Transmissíveis/tratamento farmacológico , Congressos como Assunto , Terapia Combinada , Doenças Transmissíveis/epidemiologia , Descoberta de Drogas , Avaliação Pré-Clínica de Medicamentos , Infecções por HIV/tratamento farmacológico , Humanos , Pobreza , Reino Unido
5.
Sci Rep ; 9(1): 18911, 2019 12 11.
Artigo em Inglês | MEDLINE | ID: mdl-31827124

RESUMO

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.


Assuntos
Desenvolvimento de Medicamentos , Genômica , Estudo de Associação Genômica Ampla , Humanos
7.
Nat Rev Drug Discov ; 17(5): 317-332, 2018 05.
Artigo em Inglês | MEDLINE | ID: mdl-29472638

RESUMO

A large proportion of biomedical research and the development of therapeutics is focused on a small fraction of the human genome. In a strategic effort to map the knowledge gaps around proteins encoded by the human genome and to promote the exploration of currently understudied, but potentially druggable, proteins, the US National Institutes of Health launched the Illuminating the Druggable Genome (IDG) initiative in 2014. In this article, we discuss how the systematic collection and processing of a wide array of genomic, proteomic, chemical and disease-related resource data by the IDG Knowledge Management Center have enabled the development of evidence-based criteria for tracking the target development level (TDL) of human proteins, which indicates a substantial knowledge deficit for approximately one out of three proteins in the human proteome. We then present spotlights on the TDL categories as well as key drug target classes, including G protein-coupled receptors, protein kinases and ion channels, which illustrate the nature of the unexplored opportunities for biomedical research and therapeutic development.

8.
Cell Chem Biol ; 25(2): 224-229.e2, 2018 02 15.
Artigo em Inglês | MEDLINE | ID: mdl-29276046

RESUMO

Knowledge of the full target space of bioactive substances, approved and investigational drugs as well as chemical probes, provides important insights into therapeutic potential and possible adverse effects. The existing compound-target bioactivity data resources are often incomparable due to non-standardized and heterogeneous assay types and variability in endpoint measurements. To extract higher value from the existing and future compound target-profiling data, we implemented an open-data web platform, named Drug Target Commons (DTC), which features tools for crowd-sourced compound-target bioactivity data annotation, standardization, curation, and intra-resource integration. We demonstrate the unique value of DTC with several examples related to both drug discovery and drug repurposing applications and invite researchers to join this community effort to increase the reuse and extension of compound bioactivity data.


Assuntos
Consenso , Bases de Conhecimento , Descoberta de Drogas , Interações Medicamentosas , Reposicionamento de Medicamentos , Humanos , Preparações Farmacêuticas
9.
J Chem Inf Model ; 57(12): 2976-2985, 2017 12 26.
Artigo em Inglês | MEDLINE | ID: mdl-29172488

RESUMO

Proteochemometric modeling (PCM) is a computational approach that can be considered an extension of quantitative structure-activity relationship (QSAR) modeling, where a single model incorporates information for a family of targets and all the associated ligands instead of modeling activity versus one target. This is especially useful for situations where bioactivity data exists for similar proteins but is scarce for the protein of interest. Here we demonstrate the application of PCM to identify allosteric modulators of metabotropic glutamate (mGlu) receptors. Given our long-running interest in modulating mGlu receptor function we compiled a matrix of compound-target bioactivity data. Some members of the mGlu family are well explored both internally and in the public domain, while there are much fewer examples of ligands for other targets such as the mGlu7 receptor. Using a PCM approach mGlu7 receptor hits were found. In comparison to conventional single target modeling the identified hits were more diverse, had a better confirmation rate, and provide starting points for further exploration. We conclude that the robust structure-activity relationship from well explored target family members translated to better quality hits for PCM compared to virtual screening (VS) based on a single target.


Assuntos
Regulação Alostérica/efeitos dos fármacos , Descoberta de Drogas/métodos , Relação Quantitativa Estrutura-Atividade , Receptores de Glutamato Metabotrópico/metabolismo , Sequência de Aminoácidos , Animais , Simulação por Computador , Humanos , Ligantes , Camundongos , Modelos Biológicos , Simulação de Acoplamento Molecular , Ratos , Receptores de Glutamato Metabotrópico/agonistas , Receptores de Glutamato Metabotrópico/antagonistas & inibidores , Receptores de Glutamato Metabotrópico/química
10.
Front Pharmacol ; 8: 681, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-29018348

RESUMO

Mycobacterium phenotypic hits are a good reservoir for new chemotypes for the treatment of tuberculosis. However, the absence of defined molecular targets and modes of action could lead to failure in drug development. Therefore, a combination of ligand-based and structure-based chemogenomic approaches followed by biophysical and biochemical validation have been used to identify targets for Mycobacterium tuberculosis phenotypic hits. Our approach identified EthR and InhA as targets for several hits, with some showing dual activity against these proteins. From the 35 predicted EthR inhibitors, eight exhibited an IC50 below 50 µM against M. tuberculosis EthR and three were confirmed to be also simultaneously active against InhA. Further hit validation was performed using X-ray crystallography yielding eight new crystal structures of EthR inhibitors. Although the EthR inhibitors attain their activity against M. tuberculosis by hitting yet undefined targets, these results provide new lead compounds that could be further developed to be used to potentiate the effect of EthA activated pro-drugs, such as ethionamide, thus enhancing their bactericidal effect.

11.
Sci Rep ; 7(1): 10102, 2017 08 31.
Artigo em Inglês | MEDLINE | ID: mdl-28860623

RESUMO

Protein domains mediate drug-protein interactions and this principle can guide the design of multi-target drugs i.e. polypharmacology. In this study, we associate multi-target drugs with CATH functional families through the overrepresentation of targets of those drugs in CATH functional families. Thus, we identify CATH functional families that are currently enriched in drugs (druggable CATH functional families) and we use the network properties of these druggable protein families to analyse their association with drug side effects. Analysis of selected druggable CATH functional families, enriched in drug targets, show that relatives exhibit highly conserved drug binding sites. Furthermore, relatives within druggable CATH functional families occupy central positions in a human protein functional network, cluster together forming network neighbourhoods and are less likely to be within proteins associated with drug side effects. Our results demonstrate that CATH functional families can be used to identify drug-target interactions, opening a new research direction in target identification.


Assuntos
Bases de Dados de Proteínas , Polifarmacologia , Algoritmos , Sítios de Ligação , Descoberta de Drogas/métodos , Humanos , Ligação Proteica , Análise de Sequência de Proteína/métodos
12.
PLoS Comput Biol ; 13(7): e1005641, 2017 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-28678787

RESUMO

Testing potential drug treatments in animal disease models is a decisive step of all preclinical drug discovery programs. Yet, despite the importance of such experiments for translational medicine, there have been relatively few efforts to comprehensively and consistently analyze the data produced by in vivo bioassays. This is partly due to their complexity and lack of accepted reporting standards-publicly available animal screening data are only accessible in unstructured free-text format, which hinders computational analysis. In this study, we use text mining to extract information from the descriptions of over 100,000 drug screening-related assays in rats and mice. We retrieve our dataset from ChEMBL-an open-source literature-based database focused on preclinical drug discovery. We show that in vivo assay descriptions can be effectively mined for relevant information, including experimental factors that might influence the outcome and reproducibility of animal research: genetic strains, experimental treatments, and phenotypic readouts used in the experiments. We further systematize extracted information using unsupervised language model (Word2Vec), which learns semantic similarities between terms and phrases, allowing identification of related animal models and classification of entire assay descriptions. In addition, we show that random forest models trained on features generated by Word2Vec can predict the class of drugs tested in different in vivo assays with high accuracy. Finally, we combine information mined from text with curated annotations stored in ChEMBL to investigate the patterns of usage of different animal models across a range of experiments, drug classes, and disease areas.


Assuntos
Bioensaio/métodos , Bases de Dados Factuais , Avaliação Pré-Clínica de Medicamentos/métodos , Ensaios de Triagem em Larga Escala/métodos , Aprendizado de Máquina , Processamento de Linguagem Natural , Mineração de Dados/métodos , Reprodutibilidade dos Testes , Sensibilidade e Especificidade
13.
Sci Rep ; 7: 46632, 2017 04 24.
Artigo em Inglês | MEDLINE | ID: mdl-28436422

RESUMO

Drug resistance is one of the major problems in targeted cancer therapy. A major cause of resistance is changes in the amino acids that form the drug-target binding site. Despite of the numerous efforts made to individually understand and overcome these mutations, there is a lack of comprehensive analysis of the mutational landscape that can prospectively estimate drug-resistance mutations. Here we describe and computationally validate a framework that combines the cancer-specific likelihood with the resistance impact to enable the detection of single point mutations with the highest chance to be responsible of resistance to a particular targeted cancer therapy. Moreover, for these treatment-threatening mutations, the model proposes alternative therapies overcoming the resistance. We exemplified the applicability of the model using EGFR-gefitinib treatment for Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Cancer (LSCC) and the ERK2-VTX11e treatment for melanoma and colorectal cancer. Our model correctly identified the phenotype known resistance mutations, including the classic EGFR-T790M and the ERK2-P58L/S/T mutations. Moreover, the model predicted new previously undescribed mutations as potentially responsible of drug resistance. Finally, we provided a map of the predicted sensitivity of alternative ERK2 and EGFR inhibitors, with a particular highlight of two molecules with a low predicted resistance impact.


Assuntos
Adenocarcinoma de Pulmão , Antineoplásicos/uso terapêutico , Carcinoma Pulmonar de Células não Pequenas , Sistemas de Liberação de Medicamentos/métodos , Gefitinibe/uso terapêutico , Neoplasias Pulmonares , Modelos Biológicos , Mutação Puntual , Adenocarcinoma de Pulmão/tratamento farmacológico , Adenocarcinoma de Pulmão/genética , Adenocarcinoma de Pulmão/metabolismo , Adenocarcinoma de Pulmão/patologia , Carcinoma Pulmonar de Células não Pequenas/tratamento farmacológico , Carcinoma Pulmonar de Células não Pequenas/genética , Carcinoma Pulmonar de Células não Pequenas/metabolismo , Carcinoma Pulmonar de Células não Pequenas/patologia , Humanos , Neoplasias Pulmonares/tratamento farmacológico , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/metabolismo , Neoplasias Pulmonares/patologia , Proteínas de Neoplasias/genética , Proteínas de Neoplasias/metabolismo
14.
Cochrane Database Syst Rev ; 4: CD011748, 2017 Apr 28.
Artigo em Inglês | MEDLINE | ID: mdl-28453187

RESUMO

BACKGROUND: Despite the availability of effective drug therapies that reduce low-density lipoprotein (LDL)-cholesterol (LDL-C), cardiovascular disease (CVD) remains an important cause of mortality and morbidity. Therefore, additional LDL-C reduction may be warranted, especially for patients who are unresponsive to, or unable to take, existing LDL-C-reducing therapies. By inhibiting the proprotein convertase subtilisin/kexin type 9 (PCSK9) enzyme, monoclonal antibodies (PCSK9 inhibitors) may further reduce LDL-C, potentially reducing CVD risk as well. OBJECTIVES: Primary To quantify short-term (24 weeks), medium-term (one year), and long-term (five years) effects of PCSK9 inhibitors on lipid parameters and on the incidence of CVD. Secondary To quantify the safety of PCSK9 inhibitors, with specific focus on the incidence of type 2 diabetes, cognitive function, and cancer. Additionally, to determine if specific patient subgroups were more or less likely to benefit from the use of PCSK9 inhibitors. SEARCH METHODS: We identified studies by systematically searching the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, and Web of Science. We also searched Clinicaltrials.gov and the International Clinical Trials Registry Platform and screened the reference lists of included studies. We identified the studies included in this review through electronic literature searches conducted up to May 2016, and added three large trials published in March 2017. SELECTION CRITERIA: All parallel-group and factorial randomised controlled trials (RCTs) with a follow-up time of at least 24 weeks were eligible. DATA COLLECTION AND ANALYSIS: Two review authors independently reviewed and extracted data. When data were available, we calculated pooled effect estimates. MAIN RESULTS: We included 20 studies with data on 67,237 participants (median age 61 years; range 52 to 64 years). Twelve trials randomised participants to alirocumab, three trials to bococizumab, one to RG7652, and four to evolocumab. Owing to the small number of trials using agents other than alirocumab, we did not differentiate between types of PCSK9 inhibitors used. We compared PCSK9 inhibitors with placebo (thirteen RCTs), ezetimibe (two RCTs) or ezetimibe and statins (five RCTs).Compared with placebo, PCSK9 inhibitors decreased LDL-C by 53.86% (95% confidence interval (CI) 58.64 to 49.08; eight studies; 4782 participants; GRADE: moderate) at 24 weeks; compared with ezetimibe, PCSK9 inhibitors decreased LDL-C by 30.20% (95% CI 34.18 to 26.23; two studies; 823 participants; GRADE: moderate), and compared with ezetimibe and statins, PCSK9 inhibitors decreased LDL-C by 39.20% (95% CI 56.15 to 22.26; five studies; 5376 participants; GRADE: moderate).Compared with placebo, PCSK9 inhibitors decreased the risk of CVD events, with a risk difference (RD) of 0.91% (odds ratio (OR) of 0.86, 95% CI 0.80 to 0.92; eight studies; 59,294 participants; GRADE: moderate). Compared with ezetimibe and statins, PCSK9 inhibitors appeared to have a stronger protective effect on CVD risk, although with considerable uncertainty (RD 1.06%, OR 0.45, 95% CI 0.27 to 0.75; three studies; 4770 participants; GRADE: very low). No data were available for the ezetimibe only comparison. Compared with placebo, PCSK9 probably had little or no effect on mortality (RD 0.03%, OR 1.02, 95% CI 0.91 to 1.14; 12 studies; 60,684 participants; GRADE: moderate). Compared with placebo, PCSK9 inhibitors increased the risk of any adverse events (RD 1.54%, OR 1.08, 95% CI 1.04 to 1.12; 13 studies; 54,204 participants; GRADE: low). Similar effects were observed for the comparison of ezetimibe and statins: RD 3.70%, OR 1.18, 95% CI 1.05 to 1.34; four studies; 5376 participants; GRADE: low. Clinical event data were unavailable for the ezetimibe only comparison. AUTHORS' CONCLUSIONS: Over short-term to medium-term follow-up, PCSK9 inhibitors reduced LDL-C. Studies with medium-term follow-up time (longest median follow-up recorded was 26 months) reported that PCSK9 inhibitors (compared with placebo) decreased CVD risk but may have increased the risk of any adverse events (driven by SPIRE-1 and -2 trials). Available evidence suggests that PCSK9 inhibitor use probably leads to little or no difference in mortality. Evidence on relative efficacy and safety when PCSK9 inhibitors were compared with active treatments was of low to very low quality (GRADE); follow-up times were short and events were few. Large trials with longer follow-up are needed to evaluate PCSK9 inhibitors versus active treatments as well as placebo. Owing to the predominant inclusion of high-risk patients in these studies, applicability of results to primary prevention is limited. Finally, estimated risk differences indicate that PCSK9 inhibitors only modestly change absolute risks (often to less than 1%).


Assuntos
Anticorpos Monoclonais/uso terapêutico , Doenças Cardiovasculares/prevenção & controle , LDL-Colesterol/sangue , Inibidores de PCSK9 , Prevenção Primária/métodos , Prevenção Secundária/métodos , Anticorpos Monoclonais Humanizados/uso terapêutico , Causas de Morte , Antagonistas Colinérgicos/uso terapêutico , Ezetimiba/uso terapêutico , Humanos , Inibidores de Hidroximetilglutaril-CoA Redutases/uso terapêutico , Pessoa de Meia-Idade , Ensaios Clínicos Controlados Aleatórios como Assunto , Fatores de Tempo
15.
Sci Transl Med ; 9(383)2017 03 29.
Artigo em Inglês | MEDLINE | ID: mdl-28356508

RESUMO

Target identification (determining the correct drug targets for a disease) and target validation (demonstrating an effect of target perturbation on disease biomarkers and disease end points) are important steps in drug development. Clinically relevant associations of variants in genes encoding drug targets model the effect of modifying the same targets pharmacologically. To delineate drug development (including repurposing) opportunities arising from this paradigm, we connected complex disease- and biomarker-associated loci from genome-wide association studies to an updated set of genes encoding druggable human proteins, to agents with bioactivity against these targets, and, where there were licensed drugs, to clinical indications. We used this set of genes to inform the design of a new genotyping array, which will enable association studies of druggable genes for drug target selection and validation in human disease.


Assuntos
Descoberta de Drogas , Genoma Humano , Terapia de Alvo Molecular , Reposicionamento de Medicamentos , Loci Gênicos , Estudo de Associação Genômica Ampla , Humanos , Desequilíbrio de Ligação/genética , Fenótipo , Polimorfismo de Nucleotídeo Único/genética , Reprodutibilidade dos Testes , Pesquisa Translacional Biomédica
16.
Nat Rev Drug Discov ; 16(1): 19-34, 2017 01.
Artigo em Inglês | MEDLINE | ID: mdl-27910877

RESUMO

The success of mechanism-based drug discovery depends on the definition of the drug target. This definition becomes even more important as we try to link drug response to genetic variation, understand stratified clinical efficacy and safety, rationalize the differences between drugs in the same therapeutic class and predict drug utility in patient subgroups. However, drug targets are often poorly defined in the literature, both for launched drugs and for potential therapeutic agents in discovery and development. Here, we present an updated comprehensive map of molecular targets of approved drugs. We curate a total of 893 human and pathogen-derived biomolecules through which 1,578 US FDA-approved drugs act. These biomolecules include 667 human-genome-derived proteins targeted by drugs for human disease. Analysis of these drug targets indicates the continued dominance of privileged target families across disease areas, but also the growth of novel first-in-class mechanisms, particularly in oncology. We explore the relationships between bioactivity class and clinical success, as well as the presence of orthologues between human and animal models and between pathogen and human genomes. Through the collaboration of three independent teams, we highlight some of the ongoing challenges in accurately defining the targets of molecular therapeutics and present conventions for deconvoluting the complexities of molecular pharmacology and drug efficacy.


Assuntos
Sistemas de Liberação de Medicamentos/tendências , Descoberta de Drogas/tendências , Farmacogenética/tendências , Bases de Dados de Produtos Farmacêuticos , Aprovação de Drogas , Prescrições de Medicamentos/estatística & dados numéricos , Variação Genética , Genoma Humano , Humanos , Estados Unidos , United States Food and Drug Administration
17.
Nucleic Acids Res ; 45(D1): D945-D954, 2017 01 04.
Artigo em Inglês | MEDLINE | ID: mdl-27899562

RESUMO

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.


Assuntos
Bases de Dados de Compostos Químicos , Bases de Dados de Ácidos Nucleicos , Ferramenta de Busca , Biologia Computacional/métodos , Proteção de Cultivos , Descoberta de Drogas , Ontologia Genética , Humanos , Anotação de Sequência Molecular , Farmacologia/métodos , Interface Usuário-Computador , Navegador
18.
ACS Cent Sci ; 2(10): 687-701, 2016 Oct 26.
Artigo em Inglês | MEDLINE | ID: mdl-27800551

RESUMO

The development of new antimalarial compounds remains a pivotal part of the strategy for malaria elimination. Recent large-scale phenotypic screens have provided a wealth of potential starting points for hit-to-lead campaigns. One such public set is explored, employing an open source research mechanism in which all data and ideas were shared in real time, anyone was able to participate, and patents were not sought. One chemical subseries was found to exhibit oral activity but contained a labile ester that could not be replaced without loss of activity, and the original hit exhibited remarkable sensitivity to minor structural change. A second subseries displayed high potency, including activity within gametocyte and liver stage assays, but at the cost of low solubility. As an open source research project, unexplored avenues are clearly identified and may be explored further by the community; new findings may be cumulatively added to the present work.

19.
J Biomed Semantics ; 7(1): 59, 2016 Sep 27.
Artigo em Inglês | MEDLINE | ID: mdl-27678076

RESUMO

BACKGROUND: The process of discovering new drugs is a lengthy, time-consuming and expensive process. Modern day drug discovery relies heavily on the rapid identification of novel 'targets', usually proteins that can be modulated by small molecule drugs to cure or minimise the effects of a disease. Of the 20,000 proteins currently reported as comprising the human proteome, just under a quarter of these can potentially be modulated by known small molecules Storing information in curated, actively maintained drug discovery databases can help researchers access current drug discovery information quickly. However with the increase in the amount of data generated from both experimental and in silico efforts, databases can become very large very quickly and information retrieval from them can become a challenge. The development of database tools that facilitate rapid information retrieval is important to keep up with the growth of databases. DESCRIPTION: We have developed a Gene Ontology-based navigation tool (Gene Ontology Tree) to help users retrieve biological information to single protein targets in the ChEMBL drug discovery database. 99 % of single protein targets in ChEMBL have at least one GO annotation associated with them. There are 12,500 GO terms associated to 6200 protein targets in the ChEMBL database resulting in a total of 140,000 annotations. The slim we have created, the 'ChEMBL protein target slim' allows broad categorisation of the biology of 90 % of the protein targets using just 300 high level, informative GO terms. We used the GO slim method of assigning fewer higher level GO groupings to numerous very specific lower level terms derived from the GOA to describe a set of GO terms relevant to proteins in ChEMBL. We then used the slim created to provide a web based tool that allows a quick and easy navigation of protein target space. Terms from the GO are used to capture information on protein molecular function, biological process and subcellular localisations. The ChEMBL database also provides compound information for small molecules that have been tested for their effects on these protein targets. The 'ChEMBL protein target slim' provides a means of firstly describing the biology of protein drug targets and secondly allows users to easily establish a connection between biological and chemical information regarding drugs and drug targets in ChEMBL. The 'ChEMBL protein target slim' is available as a browsable 'Gene Ontology Tree' on the ChEMBL site under the browse targets tab ( https://www.ebi.ac.uk/chembl/target/browser ). A ChEMBL protein target slim OBO file containing the GO slim terms pertinent to ChEMBL is available from the GOC website ( http://geneontology.org/page/go-slim-and-subset-guide ). CONCLUSIONS: We have created a protein target navigation tool based on the 'ChEMBL protein target slim'. The 'ChEMBL protein target slim' provides a way of browsing protein targets in ChEMBL using high level GO terms that describe the molecular functions, processes and subcellular localisations of protein drug targets in drug discovery. The tool also allows user to establish a link between ontological groupings representing protein target biology to relevant compound information in ChEMBL. We have demonstrated by the use of a simple example how the 'ChEMBL protein target slim' can be used to link biological processes with drug information based on the information in the ChEMBL database. The tool has potential to aid in areas of drug discovery such as drug repurposing studies or drug-disease-protein pathways.

20.
J Chem Inf Model ; 56(9): 1654-75, 2016 09 26.
Artigo em Inglês | MEDLINE | ID: mdl-27482722

RESUMO

Drug discovery programs frequently target members of the human kinome and try to identify small molecule protein kinase inhibitors, primarily for cancer treatment, additional indications being increasingly investigated. One of the challenges is controlling the inhibitors degree of selectivity, assessed by in vitro profiling against panels of protein kinases. We manually extracted, compiled, and standardized such profiles published in the literature: we collected 356 908 data points corresponding to 482 protein kinases, 2106 inhibitors, and 661 patents. We then analyzed this data set in terms of kinome coverage, results reproducibility, popularity, and degree of selectivity of both kinases and inhibitors. We used the data set to create robust proteochemometric models capable of predicting kinase activity (the ligand-target space was modeled with an externally validated RMSE of 0.41 ± 0.02 log units and R02 0.74 ± 0.03), in order to account for missing or unreliable measurements. The influence on the prediction quality of parameters such as number of measurements, Murcko scaffold frequency or inhibitor type was assessed. Interpretation of the models enabled to highlight inhibitors and kinases properties correlated with higher affinities, and an analysis in the context of kinases crystal structures was performed. Overall, the models quality allows the accurate prediction of kinase-inhibitor activities and their structural interpretation, thus paving the way for the rational design of compounds with a targeted selectivity profile.


Assuntos
Desenho de Fármacos , Genômica , Inibidores de Proteínas Quinases/farmacologia , Proteínas Quinases/metabolismo , Modelos Moleculares , Conformação Proteica , Inibidores de Proteínas Quinases/metabolismo , Proteínas Quinases/química , Proteínas Quinases/genética , Reprodutibilidade dos Testes , Especificidade por Substrato
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