PCSK9 monoclonal antibodies for the primary and secondary prevention of cardiovascular disease

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%)

Target identification of Mycobacterium tuberculosis phenotypic\textit{Mycobacterium tuberculosis phenotypic} hits using a concerted chemogenomic, biophysical and structural approach

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.GM is grateful to the European Molecular Biology Laboratory and Marie Sklodowska-Curie Actions for funding this work. VM and MB acknowledge Bill & Melinda Gates Foundation [subcontract by the Foundation for the National Institutes of Health (NIH)] (OPP1024021). VM and MS acknowledge the European Community’s Seventh Framework Programme [grant number 260872]. GP would like to acknowledge the Wellcome Trust and the European Molecular Biology Laboratory for funding. JPO was funded by the member nation states of the European Molecular Biology Laboratory. TLB acknowledges The Wellcome Trust for funding and support (grant number 200814/Z/16/Z)

Flipping the odds of drug development success through human genomics

Drug development depends on accurately identifying molecular targets that both play a causal role in a disease and are amenable to pharmacological action by small molecule drugs or bio-therapeutics, such as monoclonal antibodies. Errors in drug target specification contribute to the extremely high rates of drug development failure. Integrating knowledge of genes that encode druggable targets with those that influence susceptibility to common disease has the potential to radically improve the probability of drug development success

The druggable genome and support for target identification and validation in drug development

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

Global Analysis of Small Molecule Binding to Related Protein Targets

We report on the integration of pharmacological data and homology information for a large scale analysis of small molecule binding to related targets. Differences in small molecule binding have been assessed for curated pairs of human to rat orthologs and also for recently diverged human paralogs. Our analysis shows that in general, small molecule binding is conserved for pairs of human to rat orthologs. Using statistical tests, we identified a small number of cases where small molecule binding is different between human and rat, some of which had previously been reported in the literature. Knowledge of species specific pharmacology can be advantageous for drug discovery, where rats are frequently used as a model system. For human paralogs, we demonstrate a global correlation between sequence identity and the binding of small molecules with equivalent affinity. Our findings provide an initial general model relating small molecule binding and sequence divergence, containing the foundations for a general model to anticipate and predict within-target-family selectivity

Identifying Ligand Binding Conformations of the β2-Adrenergic Receptor by Using Its Agonists as Computational Probes

Recently available G-protein coupled receptor (GPCR) structures and biophysical studies suggest that the difference between the effects of various agonists and antagonists cannot be explained by single structures alone, but rather that the conformational ensembles of the proteins need to be considered. Here we use an elastic network model-guided molecular dynamics simulation protocol to generate an ensemble of conformers of a prototypical GPCR, β2-adrenergic receptor (β2AR). The resulting conformers are clustered into groups based on the conformations of the ligand binding site, and distinct conformers from each group are assessed for their binding to known agonists of β2AR. We show that the select ligands bind preferentially to different predicted conformers of β2AR, and identify a role of β2AR extracellular region as an allosteric binding site for larger drugs such as salmeterol. Thus, drugs and ligands can be used as "computational probes" to systematically identify protein conformers with likely biological significance. © 2012 Isin et al

Quantitative assessment of the expanding complementarity between public and commercial databases of bioactive compounds

<p>Abstract</p> <p>Background</p> <p>Since 2004 public cheminformatic databases and their collective functionality for exploring relationships between compounds, protein sequences, literature and assay data have advanced dramatically. In parallel, commercial sources that extract and curate such relationships from journals and patents have also been expanding. This work updates a previous comparative study of databases chosen because of their bioactive content, availability of downloads and facility to select informative subsets.</p> <p>Results</p> <p>Where they could be calculated, extracted compounds-per-journal article were in the range of 12 to 19 but compound-per-protein counts increased with document numbers. Chemical structure filtration to facilitate standardised comparisons typically reduced source counts by between 5% and 30%. The pair-wise overlaps between 23 databases and subsets were determined, as well as changes between 2006 and 2008. While all compound sets have increased, PubChem has doubled to 14.2 million. The 2008 comparison matrix shows not only overlap but also unique content across all sources. Many of the detailed differences could be attributed to individual strategies for data selection and extraction. While there was a big increase in patent-derived structures entering PubChem since 2006, GVKBIO contains over 0.8 million unique structures from this source. Venn diagrams showed extensive overlap between compounds extracted by independent expert curation from journals by GVKBIO, WOMBAT (both commercial) and BindingDB (public) but each included unique content. In contrast, the approved drug collections from GVKBIO, MDDR (commercial) and DrugBank (public) showed surprisingly low overlap. Aggregating all commercial sources established that while 1 million compounds overlapped with PubChem 1.2 million did not.</p> <p>Conclusion</p> <p>On the basis of chemical structure content <it>per se </it>public sources have covered an increasing proportion of commercial databases over the last two years. However, commercial products included in this study provide links between compounds and information from patents and journals at a larger scale than current public efforts. They also continue to capture a significant proportion of unique content. Our results thus demonstrate not only an encouraging overall expansion of data-supported bioactive chemical space but also that both commercial and public sources are complementary for its exploration.</p

Predicting enzyme targets for cancer drugs by profiling human Metabolic reactions in NCI-60 cell lines

<p>Abstract</p> <p>Background</p> <p>Drugs can influence the whole metabolic system by targeting enzymes which catalyze metabolic reactions. The existence of interactions between drugs and metabolic reactions suggests a potential way to discover drug targets.</p> <p>Results</p> <p>In this paper, we present a computational method to predict new targets for approved anti-cancer drugs by exploring drug-reaction interactions. We construct a Drug-Reaction Network to provide a global view of drug-reaction interactions and drug-pathway interactions. The recent reconstruction of the human metabolic network and development of flux analysis approaches make it possible to predict each metabolic reaction's cell line-specific flux state based on the cell line-specific gene expressions. We first profile each reaction by its flux states in NCI-60 cancer cell lines, and then propose a kernel k-nearest neighbor model to predict related metabolic reactions and enzyme targets for approved cancer drugs. We also integrate the target structure data with reaction flux profiles to predict drug targets and the area under curves can reach 0.92.</p> <p>Conclusions</p> <p>The cross validations using the methods with and without metabolic network indicate that the former method is significantly better than the latter. Further experiments show the synergism of reaction flux profiles and target structure for drug target prediction. It also implies the significant contribution of metabolic network to predict drug targets. Finally, we apply our method to predict new reactions and possible enzyme targets for cancer drugs.</p

Prediction of potential drug targets based on simple sequence properties

<p>Abstract</p> <p>Background</p> <p>During the past decades, research and development in drug discovery have attracted much attention and efforts. However, only 324 drug targets are known for clinical drugs up to now. Identifying potential drug targets is the first step in the process of modern drug discovery for developing novel therapeutic agents. Therefore, the identification and validation of new and effective drug targets are of great value for drug discovery in both academia and pharmaceutical industry. If a protein can be predicted in advance for its potential application as a drug target, the drug discovery process targeting this protein will be greatly speeded up. In the current study, based on the properties of known drug targets, we have developed a sequence-based drug target prediction method for fast identification of novel drug targets.</p> <p>Results</p> <p>Based on simple physicochemical properties extracted from protein sequences of known drug targets, several support vector machine models have been constructed in this study. The best model can distinguish currently known drug targets from non drug targets at an accuracy of 84%. Using this model, potential protein drug targets of human origin from Swiss-Prot were predicted, some of which have already attracted much attention as potential drug targets in pharmaceutical research.</p> <p>Conclusion</p> <p>We have developed a drug target prediction method based solely on protein sequence information without the knowledge of family/domain annotation, or the protein 3D structure. This method can be applied in novel drug target identification and validation, as well as genome scale drug target predictions.</p