Adjusting for treatment switching in oncology trials: A systematic review and recommendations for reporting

Objectives To systematically review the quality of reporting on the application of switching adjustment approaches in published oncology trials and industry submissions to the National Institute for Health and Care Excellence Although methods such as the rank preserving structural failure time model (RPSFTM) and inverse probability of censoring weights (IPCW) have been developed to address treatment switching, the approaches are not widely accepted within health technology assessment. This limited acceptance may partly be a consequence of poor reporting on their application. Methods Published trials and industry submissions were obtained from searches of PubMed and nice.org.uk, respectively. The quality of reporting in these studies was judged against a checklist of reporting recommendations, which was developed by the authors based on detailed considerations of the methods. Results Thirteen published trials and 8 submissions to nice.org.uk satisfied inclusion criteria. The quality of reporting around the implementation of the RPSFTM and IPCW methods was generally poor. Few studies stated whether the adjustment approach was prespecified, more than a third failed to provide any justification for the chosen method, and nearly half neglected to perform sensitivity analyses. Further, it was often unclear how the RPSFTM and IPCW methods were implemented. Conclusions Inadequate reporting on the application of switching adjustment methods increases uncertainty around results, which may contribute to the limited acceptance of these methods by decision makers. The proposed reporting recommendations aim to support the improved interpretation of analyses undertaken to adjust for treatment switching

The effect of molecular fields, lattice spacing and analysis options on CoMFA predictive ability

Comparative Molecular Field Analysis (CoMFA) is a popular Three-Dimensional Quantitative Structure–Activity Relationship (3D-QSAR) method. The effect of varying molecular fields [CoMFA vs. Comparative Molecular Similarity Indices Analysis (CoMSIA)], lattice spacing and analysis options on predictive performance was assessed based on cross validated R2 values of 30 datasets taken from the literature. The CoMFA method results in statistically significantly higher cross validated R2 values compared to CoMSIA when only steric and electrostatic fields are used. When the hydrophobic molecular field is included in the CoMSIA analysis (as is most commonly the case) the difference between CoMFA and CoMSIA is no longer statistically significant. Addition of hydrogen bond field does not improve CoMFA predictivity. Although there was a trend towards increased predictivity with decreased lattice spacing, this was not statistically significant. Altering the default filtering criteria and cut-off values for Lennard Jones and Coulomb fields also did not generally result in a statistically significant effect on predictive performance.Ruchi R. Mittal, Ross A. McKinnon and Michael  J. Soric

Comparison data sets for benchmarking QSAR methodologies in lead optimization

2D and 3D QSAR techniques are widely used in lead optimization-like processes. A compilation of 40 diverse data sets is described. It is proposed that these can be used as a common benchmark sample for comparisons of QSAR methodologies, primarily in terms of predictive ability. Use of this benchmark set will be useful for both assessment of new methods and for optimization of existing methods.Ruchi R. Mittal, Ross A. McKinnon and Michael J. Soric

Effect of steric molecular field settings on CoMFA predictivity

Steric molecular field can be represented in a number of ways in comparative molecular field analysis (CoMFA). This study aimed to investigate whether the choice of steric molecular field settings significantly influences the predictive performance of CoMFA and, if so, which is the best. The three-dimensional quantitative structure activity relationship (3D-QSAR) models based on Lennard–Jones, indicator, parabolic and Gaussian steric fields were compared using 28 datasets taken from the literature. The analysis of the predictive ability of these models (cross validated R2) indicates that steric fields in which the value drops off quickly with distance (i.e. Lennard–Jones and indicator fields) tend to perform better than the Gaussian version, which has a slower and smoother decrease. Furthermore, depending on the steric field type used, the field sampling density (i.e. grid spacing) has a variable influence on the predictive ability of the models generated.Ruchi R. Mittal, Ross A. McKinnon, Michael J. Soric

Systematic statistical comparison of comparative molecular similarity indices analysis molecular fields for computer-aided lead optimization

Comparative molecular similarity indices analysis (CoMSIA) is a 3D quantitative structure−activity relationship technique used to determine structural and electronic features influencing biological activity. This proves particularly useful for facilitating lead optimization projects. This study aimed to compare CoMSIA models produced using different subsets of the CoMSIA molecular fields (steric, electrostatic, hydrophobic, hydrogen-bond donor, and hydrogen-bond acceptor) in a systematic and statistically valid manner. A total of 23 data sets sourced from the literature were used to compare molecular field contribution and model predictivity using leave-one-out cross-validated R2 values. Predictive ability varied in a highly statistically significant manner depending on the set of CoMSIA molecular fields used. In general, the greater the number of CoMSIA molecular fields included in the analysis, the better the model predictivity was. There is great redundancy in the information contained in the different CoMSIA molecular fields. When all five CoMSIA molecular fields are included, the hydrophobic and electrostatic fields had the largest and the steric field the smallest contribution. Data sets were clustered into four groups on the basis of the utility of molecular field sets to generate predictive models.Mafalda M. Dias, Ruchi R. Mittal, Ross A. McKinnon, and Michael J. Soric

Partial charge calculation method affects CoMFA QSAR prediction accuracy

The 3D-QSAR method comparative molecular field analysis (CoMFA) involves the estimation of atomic partial charges as part of the process of calculating molecular electrostatic fields. Using 30 data sets from the literature the effect of using different common partial charge calculation methods on the predictivity (cross-validated R2) of CoMFA was studied. The partial charge methods ranged from the popular Gasteiger and the newer MMFF94 electronegativity equalization methods, to the more complex and computationally expensive semiempirical charges AM1, MNDO, and PM3. The MMFF94 and semiempirical MNDO, AM1, and PM3 methods for computing charges were found to result in statistically significantly more predictive CoMFA models than the Gasteiger charges. Although there was a trend toward the semiempirical charges performing better than the MMFF94 charges, the difference was not statistically significant. Thus, semiempirical partial charge calculation methods are suggested for the most predictive CoMFA models, but the MMFF94 charge calculation method is a very good alternative if semiempirical methods are not available or faster calculation speed is important.Ruchi R. Mittal, Lisa Harris, Ross A. McKinnon and Michael J. Soric

Increased abundance of Sutterella spp. and Ruminococcus torques in feces of children with autism spectrum disorder

Background: A recent report indicated that numbers of Sutterella spp. are elevated in gastrointestinal biopsies taken from children with autism spectrum disorder (ASD). We have recently reported changes in the numbers of some bacteria within the stool of ASD children, and now examine whether numbers of Sutterella spp. and some other mucosa-associated bacteria linked with gastrointestinal disease (Ruminococcus gnavus and Ruminococcus torques) are also altered in the stool of these children. Findings. We show that numbers of Sutterella spp. are elevated in feces of ASD children relative to controls, and that numbers of R. torques are higher in the children with ASD with a reported functional gastrointestinal disorder than those without such a disorder. Conclusions: We show further evidence of changes in the gut microbiota of children with ASD and confirm that the abundance of Sutterella spp. is altered in stool. © 2013 Wang et al.; licensee Bio Med Central Ltd

Low risk of hyperprogression with first-line chemoimmunotherapy for advanced non-small cell lung cancer: pooled analysis of 7 clinical trials

Background Monotherapy immune checkpoint inhibitor (ICI) used in second- or later-line settings has been reported to induce hyperprogression. This study evaluated hyperprogression risk with ICI (atezolizumab) in the first-, second-, or later-line treatment of advanced non-small cell lung cancer (NSCLC), and provides insights into hyperprogression risk with contemporary first-line ICI treatment. Methods Hyperprogression was identified using Response Evaluation Criteria in Solid Tumours (RECIST)-based criteria in a dataset of pooled individual-participant level data from BIRCH, FIR, IMpower130, IMpower131, IMpower150, OAK, and POPLAR trials. Odds ratios were computed to compare hyperprogression risks between groups. Landmark Cox proportional-hazard regression was used to evaluate the association between hyperprogression and progression-free survival/overall survival. Secondarily, putative risk factors for hyperprogression among second- or later-line atezolizumab-treated patients were evaluated using univariate logistic regression models. Results Of the included 4644 patients, 119 of the atezolizumab-treated patients (n = 3129) experienced hyperprogression. Hyperprogression risk was markedly lower with first-line atezolizumab-either chemoimmunotherapy or monotherapy-compared to second/later-line atezolizumab monotherapy (0.7% vs. 8.8%, OR = 0.07, 95% CI, 0.04-0.13). Further, there was no statistically significant difference in hyperprogression risk with first-line atezolizumab-chemoimmunotherapy versus chemotherapy alone (0.6% vs. 1.0%, OR = 0.55, 95% CI, 0.22-1.36). Sensitivity analyses using an extended RECIST-based criteria including early death supported these findings. Hyperprogression was associated with worsened overall survival (HR = 3.4, 95% CI, 2.7-4.2, P < .001); elevated neutrophil-to-lymphocyte ratio was the strongest risk factor for hyperprogression (C-statistic = 0.62, P < .001). Conclusions This study presents first evidence for a markedly lower hyperprogression risk in advanced NSCLC patients treated with first-line ICI, particularly with chemoimmunotherapy, as compared to second- or later-line ICI treatment. This study evaluated hyperprogression risk with the use of immune checkpoint inhibitor (ICI) in the first-, second-, or later-line treatment of advanced non-small cell lung cancer, providing insight into hyperprogression risk with contemporary first-line ICI treatment

Towards integrated ADME prediction: past, present and future directions for modelling metabolism by UDP-glucoronosyltransferases

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