Refractive index measurement of pharmaceutical powders in the short-wave infrared range using index matching assisted with phase imaging

Refractive index is a fundamental optical property of powder and a key input to the measurement of the size distribution using light scattering and the measurement of the absorption and scattering coefficients using diffuse reflectance spectroscopy. In this work, we demonstrate a method that combines index matching with phase imaging to determine the refractive index of complex powder particles at high accuracy without relying on any assumptions. Index matching is robust, but the accuracy is not high by relying on ocular observation of the interface. Phase imaging using interferometry improves the accuracy of determining the match by an order of magnitude. Using the proposed method, we determine the refractive index of four types of pharmaceutical powder in the short-wave infrared range of 1100-1650 nm

Identification of Cell Culture Factors Influencing Afucosylation Levels in Monoclonal Antibodies by Partial Least-Squares Regression and Variable Importance Metrics

Retrospective analysis of historic data for cell culture processes is a powerful tool to develop further process understanding. In particular, deploying retrospective analyses can identify important cell culture process parameters for controlling critical quality attributes, e.g., afucosylation, for the production of monoclonal antibodies (mAbs). However, a challenge of analyzing large cell culture data is the high correlation between regressors (particularly media composition), which makes traditional analyses, such as analysis of variance and multivariate linear regression, inappropriate. Instead, partial least-squares regression (PLSR) models, in combination with machine learning techniques such as variable importance metrics, are an orthogonal or alternative approach to identifying important regressors and overcoming the challenge of a highly covariant data structure. A specific workflow for the retrospective analysis of cell culture data is proposed that covers data curation, PLS regression, model analysis, and further steps. In this study, the proposed workflow was applied to data from four mAb products in an industrial cell culture process to identify significant process parameters that influence the afucosylation levels. The PLSR workflow successfully identified several significant parameters, such as temperature and media composition, to enhance process understanding of the relationship between cell culture processes and afucosylation levels

Observational and genetic associations between cardiorespiratory fitness and cancer:a UK Biobank and international consortia study

Background: The association of fitness with cancer risk is not clear. Methods: We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for risk of lung, colorectal, endometrial, breast, and prostate cancer in a subset of UK Biobank participants who completed a submaximal fitness test in 2009-12 (N = 72,572). We also investigated relationships using two-sample Mendelian randomisation (MR), odds ratios (ORs) were estimated using the inverse-variance weighted method.Results: After a median of 11 years of follow-up, 4290 cancers of interest were diagnosed. A 3.5 ml O2⋅min−1⋅kg−1 total-body mass increase in fitness (equivalent to 1 metabolic equivalent of task (MET), approximately 0.5 standard deviation (SD)) was associated with lower risks of endometrial (HR = 0.81, 95% CI: 0.73–0.89), colorectal (0.94, 0.90–0.99), and breast cancer (0.96, 0.92–0.99). In MR analyses, a 0.5 SD increase in genetically predicted O2⋅min−1⋅kg−1 fat-free mass was associated with a lower risk of breast cancer (OR = 0.92, 95% CI: 0.86–0.98). After adjusting for adiposity, both the observational and genetic associations were attenuated. Discussion: Higher fitness levels may reduce risks of endometrial, colorectal, and breast cancer, though relationships with adiposity are complex and may mediate these relationships. Increasing fitness, including via changes in body composition, may be an effective strategy for cancer prevention.</p

Genetically proxied glucose-lowering drug target perturbation and risk of cancer: a Mendelian randomisation analysis

Aims/hypothesis: Epidemiological studies have generated conflicting findings on the relationship between glucose-lowering medication use and cancer risk. Naturally occurring variation in genes encoding glucose-lowering drug targets can be used to investigate the effect of their pharmacological perturbation on cancer risk. Methods: We developed genetic instruments for three glucose-lowering drug targets (peroxisome proliferator activated receptor γ [PPARG]; sulfonylurea receptor 1 [ATP binding cassette subfamily C member 8 (ABCC8)]; glucagon-like peptide 1 receptor [GLP1R]) using summary genetic association data from a genome-wide association study of type 2 diabetes in 148,726 cases and 965,732 controls in the Million Veteran Program. Genetic instruments were constructed using cis-acting genome-wide significant (p−8) SNPs permitted to be in weak linkage disequilibrium (r 21c: OR 1.75 [95% CI 1.07, 2.85], p=0.02). In histological subtype-stratified analyses, genetically proxied PPARG perturbation was weakly associated with lower risk of oestrogen receptor-positive breast cancer (OR 0.57 [95% CI 0.38, 0.85], p=6.45×10−3). In co-localisation analysis, however, there was little evidence of shared causal variants for type 2 diabetes liability and cancer endpoints in the PPARG locus, although these analyses were likely underpowered. There was little evidence to support associations between genetically proxied PPARG perturbation and colorectal or overall cancer risk or between genetically proxied ABCC8 or GLP1R perturbation with risk across cancer endpoints. Conclusions/interpretation: Our drug target MR analyses did not find consistent evidence to support an association of genetically proxied PPARG, ABCC8 or GLP1R perturbation with breast, colorectal, prostate or overall cancer risk. Further evaluation of these drug targets using alternative molecular epidemiological approaches may help to further corroborate the findings presented in this analysis. Data availability: Summary genetic association data for select cancer endpoints were obtained from the public domain: breast cancer (https://bcac.ccge.medschl.cam.ac.uk/bcacdata/); and overall prostate cancer (http://practical.icr.ac.uk/blog/). Summary genetic association data for colorectal cancer can be accessed by contacting GECCO (kafdem at fredhutch.org). Summary genetic association data on advanced prostate cancer can be accessed by contacting PRACTICAL (practical at icr.ac.uk). Summary genetic association data on type 2 diabetes from Vujkovic et al (Nat Genet, 2020) can be accessed through dbGAP under accession number phs001672.v3.p1 (pha004945.1 refers to the European-specific summary statistics). UK Biobank data can be accessed by registering with UK Biobank and completing the registration form in the Access Management System (AMS) (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access).</p