5,879 research outputs found
Human Genomics and Drug Development
Insights into the genetic basis of human disease are helping to address some of the key challenges in new drug development including the very high rates of failure. Here we review the recent history of an emerging, genomics-assisted approach to pharmaceutical research and development, and its relationship to Mendelian randomization (MR), a well-established analytical approach to causal inference. We demonstrate how human genomic data linked to pharmaceutically relevant phenotypes can be used for (1) drug target identification (mapping relevant drug targets to diseases), (2) drug target validation (inferring the likely effects of drug target perturbation), (3) evaluation of the effectiveness and specificity of compound-target engagement (inferring the extent to which the effects of a compound are exclusive to the target and distinguishing between on-target and off-target compound effects), and (4) the selection of end points in clinical trials (the diseases or conditions to be evaluated as trial outcomes). We show how genomics can help identify indication expansion opportunities for licensed drugs and repurposing of compounds developed to clinical phase that proved safe but ineffective for the original intended indication. We outline statistical and biological considerations in using MR for drug target validation (drug target MR) and discuss the obstacles and challenges for scaled applications of these genomics-based approaches
Cochrane corner: PCSK9 monoclonal antibodies for the primary and secondary prevention of cardiovascular disease
Introduction:
Drug therapies targeted at the reduction of low-density lipoproteincholesterol (LDL-C) are mainstream in the treatment of cardiovascular disease (CVD) and particularly for the prevention of coronary heart disease. In patients who do not have a sufficient response to, or who do not tolerate traditional LDL-C-lowering therapies such as statins or ezetimibe, monoclonal antibodies (mAbs) against PCSK9 (PCSK9 inhibitors) may provide an alternative treatment. Non-mAb-based PCSK9 inhibitors such as inclisiran are also emerging but currently lack robust outcome data1 and their effects are not considered in the current review. In this synopsis, we summarise findings from a recent update of a Cochrane systematic review on the efficacy and safety of PCSK9 inhibitors.2 This article focuses on the effects on outcomes (CVD and total mortality), safety, and the quality of the evidence in studies of mAb PCSK9 inhibitors alirocumab and evolocumab. Most of the available studies compared PCSK9 mAb treatment against placebo (against a background of usual care including statin and or ezetimibe), with a smaller group of studies evaluating the effects of PCSK9 mAb directly against statins and/or ezetimibe (none of the trials compared PCSK9 exclusively against statin treatment).
Methods:
The following databases were systematically searched for suitable randomised controlled trials (RCTs): Cochrane Central Register of Controlled Trials, MEDLINE, Embase, Web of Science, ClinicalTrials.gov and the International Clinical Trials Registry Platform. Parallel-group and factorial RCTs with at least 24 weeks of follow-up were eligible; due to discontinuation of bococizumab and RG7652, studies examining these mAbs were excluded in this update.
Summary of findings
The 24 selected randomised trials (60 997 participants, box 1) predominantly included high-risk patients, for example, by enrolling patients with non-optimal LDL-C concentration despite treatment with statins or ezetimibe, or with a history of CVD. The study sample included 1879 who had familial hypercholesterolaemia (FH) (22% of the alirocumab participants and 38% of the evolocumab participants who provided information on FH status), and 18 908 (31%) with a diagnosis of type 2 diabetes mellitus (T2DM) at baseline (32% in alirocumab and 34% evolocumab trials; out of participants with reported T2DM status). Of the included patients, 4590 had no history of CVD (10% of the alirocumab patients and 7% of the evolocumab participants). Alirocumab was evaluated in 18 trials and evolocumab in 6 trials. Comparisons were made against placebo in 18 trials, ezetimibe and/or statins in 6 trials. Tables 1 and 2 display the key results of the meta-analysis for both PCSK9 inhibitors compared with placebo and with statins and/or ezetimibe, respectively
Lipid lowering and Alzheimer's disease risk: a Mendelian randomization study
Objective: To examine whether genetic variation affecting the expression or function of lipid-lowering drug targets isassociated with Alzheimer disease (AD) risk, to evaluate the potential impact of long-term exposure to correspondingtherapeutics.Methods: We conducted Mendelian randomization analyses using variants in genes that encode the protein targets ofseveral approved lipid-lowering drug classes: HMGCR (encoding the target for statins), PCSK9 (encoding the target forPCSK9 inhibitors, eg, evolocumab and alirocumab), NPC1L1 (encoding the target for ezetimibe), and APOB (encodingthe target of mipomersen). Variants were weighted by associations with low-density lipoprotein cholesterol (LDL-C)using data from lipid genetics consortia (n up to 295,826). We meta-analyzed Mendelian randomization estimates forregional variants weighted by LDL-C on AD risk from 2 large samples (total n = 24,718 cases, 56,685 controls).Results: Models for HMGCR, APOB, and NPC1L1 did not suggest that the use of related lipid-lowering drug classeswould affect AD risk. In contrast, genetically instrumented exposure to PCSK9 inhibitors was predicted to increase ADrisk in both of the AD samples (combined odds ratio per standard deviation lower LDL-C inducible by the drug tar-get = 1.45, 95% confidence interval = 1.23–1.69). This risk increase was opposite to, although more modest than, thedegree of protection from coronary artery disease predicted by these same methods for PCSK9 inhibition.Interpretation: We did not identify genetic support for the repurposing of statins, ezetimibe, or mipomersen for ADprevention. Notwithstanding caveats to this genetic evidence, pharmacovigilance for AD risk among users of PCSK9inhibitors may be warranted
Polygenic risk scores for coronary artery disease and subsequent event risk amongst established cases
BACKGROUND: There is growing evidence that polygenic risk scores (PRS) can identify individuals with elevated lifetime risk of coronary artery disease (CAD). Whether they can also be used to stratify risk of subsequent events among those surviving a first CAD event remains uncertain, with possible biological differences between CAD onset and progression, and the potential for index event bias. METHODS: Using two baseline subsamples of UK Biobank; prevalent CAD cases (N = 10 287) and individuals without CAD (N = 393 108), we evaluated associations between a CAD PRS and incident cardiovascular and fatal outcomes. RESULTS: A 1 S.D. higher PRS was associated with increased risk of incident MI in participants without CAD (OR 1.33; 95% C.I. 1.29, 1.38), but the effect estimate was markedly attenuated in those with prevalent CAD (OR 1.15; 95% C.I. 1.06, 1.25); heterogeneity P = 0.0012. Additionally, among prevalent CAD cases, we found evidence of an inverse association between the CAD PRS and risk of all-cause death (OR 0.91; 95% C.I. 0.85, 0.98) compared to those without CAD (OR 1.01; 95% C.I. 0.99, 1.03); heterogeneity P = 0.0041. A similar inverse association was found for ischaemic stroke (Prevalent CAD (OR 0.78; 95% C.I. 0.67, 0.90); without CAD (OR 1.09; 95% C.I. 1.04, 1.15), heterogeneity P < 0.001). CONCLUSIONS: Bias induced by case stratification and survival into UK Biobank may distort associations of polygenic risk scores derived from case-control studies or populations initially free of disease. Differentiating between effects of possible biases and genuine biological heterogeneity is a major challenge in disease progression research
What you know can influence what you are going to know (especially for older adults)
Stimuli related to an individual's knowledge/experience are often more memorable than abstract stimuli, particularly for older adults. This has been found when material that is congruent with knowledge is contrasted with material that is incongruent with knowledge, but there is little research on a possible graded effect of congruency. The present study manipulated the degree of congruency of study material with participants’ knowledge. Young and older participants associated two famous names to nonfamous faces, where the similarity between the nonfamous faces and the real famous individuals varied. These associations were incrementally easier to remember as the name-face combinations became more congruent with prior knowledge, demonstrating a graded congruency effect, as opposed to an effect based simply on the presence or absence of associations to prior knowledge. Older adults tended to show greater susceptibility to the effect than young adults, with a significant age difference for extreme stimuli, in line with previous literature showing that schematic support in memory tasks particularly benefits older adults
The formation of voids in a universe with cold dark matter and a cosmological constant
A spherical Lagrangian hydrodynamical code has been written to study the
formation of cosmological structures in the early Universe. In this code we
take into account the presence of collisionless non-baryonic cold dark matter
(CDM), the cosmological constant and a series of physical processes present
during and after the recombination era, such as photon drag resulting from the
cosmic background radiation and hydrogen molecular production. We follow the
evolution of the structure since the recombination era until the present epoch.
As an application of this code we study the formation of voids starting from
negative density perturbations which evolved during and after the recombination
era. We analyse a set of COBE-normalized models, using different spectra to see
their influence on the formation of voids. Our results show that large voids
with diameters ranging from 10h^{-1} Mpc up to 50h^{-1} Mpc can be formed in a
universe model dominated by the cosmological constant (\Omega_\Lambda ~ 0.8).
This particular scenario is capable of forming large and deep empty regions
(with density contrasts \delta < -0.6). Our results also show that the physical
processes acting on the baryonic matter produce a transition region where the
radius of the dark matter component is greater than the baryonic void radius.
The thickness of this transition region ranges from about tens of kiloparsecs
up to a few megaparsecs, depending on the spectrum considered. Putative objects
formed near voids and within the transition region would have a different
amount of baryonic/dark matter when compared with \Omega_b/\Omega_d. If one
were to use these galaxies to determine, by dynamical effects or other
techniques, the quantity of dark matter present in the Universe, the result
obtained would be only local and not representative of the Universe as a whole.Comment: MNRAS (in press); 9 pages, no figure
Cochrane corner: PCSK9 monoclonal antibodies for the primary and secondary prevention of cardiovascular disease
In this synopsis we describe findings from a recent Cochrane review on PCSK9 inhibitors for cardiovascular disease prevention. Compared against placebo, PCSK9 inhibitors show a substantial reduction in atherogenic lipid particles (LDL-C, Apo-B and Lp(a)), and protective effects on: CVD, MI, stroke, and elevated creatinine. There is however only limited, and lower quality, evidence comparing PCSK9 inhibitors against active treatments such as statins or ezetimibe. Furthermore, the current evidence is limited by the relatively short follow-up (at most a median follow-up of 26 months) which likely also relates to the observed beneficial safety profile, showing no clear signals on adverse events such as influenza, myalgia, T2DM or cancers
Triglyceride-containing lipoprotein sub-fractions and risk of coronary heart disease and stroke: A prospective analysis in 11,560 adults
AIMS: Elevated low-density lipoprotein cholesterol (LDL-C) is a risk factor for cardiovascular disease; however, there is uncertainty about the role of total triglycerides and the individual triglyceride-containing lipoprotein sub-fractions. We measured 14 triglyceride-containing lipoprotein sub-fractions using nuclear magnetic resonance and examined associations with coronary heart disease and stroke. METHODS: Triglyceride-containing sub-fraction measures were available in 11,560 participants from the three UK cohorts free of coronary heart disease and stroke at baseline. Multivariable logistic regression was used to estimate the association of each sub-fraction with coronary heart disease and stroke expressed as the odds ratio per standard deviation increment in the corresponding measure. RESULTS: The 14 triglyceride-containing sub-fractions were positively correlated with one another and with total triglycerides, and inversely correlated with high-density lipoprotein cholesterol (HDL-C). Thirteen sub-fractions were positively associated with coronary heart disease (odds ratio in the range 1.12 to 1.22), with the effect estimates for coronary heart disease being comparable in subgroup analysis of participants with and without type 2 diabetes, and were attenuated after adjustment for HDL-C and LDL-C. There was no evidence for a clear association of any triglyceride lipoprotein sub-fraction with stroke. CONCLUSIONS: Triglyceride sub-fractions are associated with increased risk of coronary heart disease but not stroke, with attenuation of effects on adjustment for HDL-C and LDL-C
Genetic drug target validation using Mendelian randomisation
Mendelian randomisation (MR) analysis is an important tool to elucidate the causal relevance of environmental and biological risk factors for disease. However, causal inference is undermined if genetic variants used to instrument a risk factor also influence alternative disease-pathways (horizontal pleiotropy). Here we report how the 'no horizontal pleiotropy assumption' is strengthened when proteins are the risk factors of interest. Proteins are typically the proximal effectors of biological processes encoded in the genome. Moreover, proteins are the targets of most medicines, so MR studies of drug targets are becoming a fundamental tool in drug development. To enable such studies, we introduce a mathematical framework that contrasts MR analysis of proteins with that of risk factors located more distally in the causal chain from gene to disease. We illustrate key model decisions and introduce an analytical framework for maximising power and evaluating the robustness of analyses
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