The pharmaceutical industry remains under huge pressure to address the high attrition rates in drug development. Attempts to reduce the number of efficacy- and safety-related failures by analysing ...possible links to the physicochemical properties of small-molecule drug candidates have been inconclusive because of the limited size of data sets from individual companies. Here, we describe the compilation and analysis of combined data on the attrition of drug candidates from AstraZeneca, Eli Lilly and Company, GlaxoSmithKline and Pfizer. The analysis reaffirms that control of physicochemical properties during compound optimization is beneficial in identifying compounds of candidate drug quality and indicates for the first time a link between the physicochemical properties of compounds and clinical failure due to safety issues. The results also suggest that further control of physicochemical properties is unlikely to have a significant effect on attrition rates and that additional work is required to address safety-related failures. Further cross-company collaborations will be crucial to future progress in this area.
The statistical metrics used to characterize the external predictivity of a model, i.e., how well it predicts the properties of an independent test set, have proliferated over the past decade. This ...paper clarifies some apparent confusion over the use of the coefficient of determination, R(2), as a measure of model fit and predictive power in QSAR and QSPR modeling. R(2) (or r(2)) has been used in various contexts in the literature in conjunction with training and test data for both ordinary linear regression and regression through the origin as well as with linear and nonlinear regression models. We analyze the widely adopted model fit criteria suggested by Golbraikh and Tropsha ( J. Mol. Graphics Modell. 2002 , 20 , 269 - 276 ) in a strict statistical manner. Shortcomings in these criteria are identified, and a clearer and simpler alternative method to characterize model predictivity is provided. The intent is not to repeat the well-documented arguments for model validation using test data but rather to guide the application of R(2) as a model fit statistic. Examples are used to illustrate both correct and incorrect uses of R(2). Reporting the root-mean-square error or equivalent measures of dispersion, which are typically of more practical importance than R(2), is also encouraged, and important challenges in addressing the needs of different categories of users such as computational chemists, experimental scientists, and regulatory decision support specialists are outlined.
Valentin Amrhein, Sander Greenland, Blake McShane and more than 800 signatories call for an end to hyped claims and the dismissal of possibly crucial effects.
Narrow-sense heritability (h
) is an important genetic parameter that quantifies the proportion of phenotypic variance in a trait attributable to the additive genetic variation generated by all ...causal variants. Estimation of h
previously relied on closely related individuals, but recent developments allow estimation of the variance explained by all SNPs used in a genome-wide association study (GWAS) in conventionally unrelated individuals, that is, the SNP-based heritability (). In this Perspective, we discuss recently developed methods to estimate for a complex trait (and genetic correlation between traits) using individual-level or summary GWAS data. We discuss issues that could influence the accuracy of , definitions, assumptions and interpretations of the models, and pitfalls of misusing the methods and misinterpreting the models and results.
While A1C is well established as an important risk marker for diabetes complications, with the increasing use of continuous glucose monitoring (CGM) to help facilitate safe and effective diabetes ...management, it is important to understand how CGM metrics, such as mean glucose, and A1C correlate. Estimated A1C (eA1C) is a measure converting the mean glucose from CGM or self-monitored blood glucose readings, using a formula derived from glucose readings from a population of individuals, into an estimate of a simultaneously measured laboratory A1C. Many patients and clinicians find the eA1C to be a helpful educational tool, but others are often confused or even frustrated if the eA1C and laboratory-measured A1C do not agree. In the U.S., the Food and Drug Administration determined that the nomenclature of eA1C needed to change. This led the authors to work toward a multipart solution to facilitate the retention of such a metric, which includes renaming the eA1C the glucose management indicator (GMI) and generating a new formula for converting CGM-derived mean glucose to GMI based on recent clinical trials using the most accurate CGM systems available. The final aspect of ensuring a smooth transition from the old eA1C to the new GMI is providing new CGM analyses and explanations to further understand how to interpret GMI and use it most effectively in clinical practice. This Perspective will address why a new name for eA1C was needed, why GMI was selected as the new name, how GMI is calculated, and how to understand and explain GMI if one chooses to use GMI as a tool in diabetes education or management.
Programming tools: Adventures with R Tippmann, Sylvia
Nature (London),
2015-Jan-01, 2015-01-01, 20150101, Volume:
517, Issue:
7532
Journal Article
Peer reviewed
Open access
But she was also following a wider trend: for many academics seeking to wean themselves offcommercial software, R is the data-analysis tool of choice. Besides being free, R is popular partly because ...it presents different faces to different users.
The global burden of chronic pain is projected to be large and growing, in concert with the burden of noncommunicable diseases. This is the first systematic review and meta-analysis of the prevalence ...of chronic pain without clear etiology in general, elderly, and working populations of low- and middle-income countries (LMICs).
We collected and reported data using Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, excluding acute pain or pain associated with a concurrent medical condition. One hundred nineteen publications in 28 LMICs were identified for systematic review; the 68 reports that focused on general adult populations (GP), elderly general populations (EGP), or workers (W) were evaluated using mixed-effects regression meta-analysis.
Average chronic pain prevalence is reported as a percentage of the population, with 95% confidence interval for each pain type and population (GP, EGP, and W; NA is equal to not available): unspecified chronic pain (3426-42, 6241-81, and NA); low back pain (2115-27, 2816-42, and 5226-77); headache (4227-58, 3019-43, and 5113-88); chronic daily headache (53-7, 51-12, and 100-33); chronic migraine (GP 126-19); chronic tension type headache (GP 83-15); musculoskeletal pain (2519-33, 4428-62, and 7960-94); joint pain (1411-18, 3416-54, and NA); chronic pelvic/prostatitis pain (GP 40-14); temporomandibular disorder (354-78, 80-24, and NA); abdominal pain (EGP 176-32); fibromyalgia (Combined GP, EGP, W 65-7); and widespread pain (71-18, 198-32, and NA). Chronic low back pain and musculoskeletal pain were 2.50 (1.21-4.10) and 3.11 (2.13-4.37) times more prevalent among W, relative to a GP. Musculoskeletal, joint, and unspecified pain were 1.74 (1.03-2.69), 2.36 (1.09-4.02), and 1.83 (1.13-2.65) times more prevalent among the EGP, relative to a GP. There was significant heterogeneity among studies for all pain types (I > 90%).
Chronic pain is prevalent in LMICs, and where there was sufficient evidence, generally more prevalent in EGP and W. This meta-analysis reveals the spectrum of chronic pain without clear etiology in LMICs. Steps should be taken to reduce heterogeneity in the assessment of global chronic pain. Possible actions may include standardization of chronic pain definition, widespread adoption of validated questionnaires across cultures, attention to inequitably burdened populations, and inclusion of queries regarding known associations of chronic pain with social and psychological factors that, in combination, increase the global burden of noncommunicable disease and disability.
The article challenges the notion that below-replacement fertility and its local variation in China are primarily attributable to the government's birth planning policy. Data from the 2000 census and ...provincial statistical yearbooks are used to compare fertility in Jiangsu and Zhejiang, two of the most developed provinces in China, to examine the relationship between socioeconomic development and low fertility. The article demonstrates that although low fertility in China was achieved under the government's restrictive one-child policy, structural changes brought about by socioeconomic development and ideational shifts accompanying the new wave of globalization played a key role in China's fertility reduction.
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