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In past years many models describing growth and inactivation of microorganisms have been developed. This study is a discussion of the growth and inactivation models that can be used in a stepwise procedure for quantitative risk assessment. First, rough risk assessments are performed in which orders of magnitude for microbial processes are estimated by the use of simple models. This method provides an efficient way to find the main determinants of risk. Second, the main determinants of risk are studied more accurately and quantitatively. It is best to compare several models at this level, as no model is expected to be able accurately to predict microbial responses under all circumstances. By comparing various models the main determinants of risk are studied from several points of view, and risks can be assessed on a broad basis. If, however, process variations have a more profound effect on risk than the differences between models, it is most efficient to use the simplest model available. If relevant, the process variations can be stochastically described in the third level of detail. Stochastic description of the process parameters will however not change the conclusion on the usefulness of simple models in quantitative risk assessments. The proposed stepwise procedure that starts simply before going into detail provides a structured method of risk assessment and prevents the researcher from getting caught in too much complexity. This simplicity is necessary because of the complex nature of food safety. The principal aspects are highlighted during the procedure and many factors can be omitted since their quantitative effect is negligible.

Background Human epidermal growth factor 2 (HER2/ERBB2) is frequently amplified/mutated in cancer. The tyrosine kinase inhibitors (TKIs) lapatinib, neratinib, and tucatinib are FDA-approved for the treatment of HER2-positive breast cancer. Direct comparisons of the preclinical efficacy of the TKIs have been limited to small-scale studies. Novel biomarkers are required to define beneficial patient populations. Methods In this study, the anti-proliferative effects of the three TKIs were directly compared using a 115 cancer cell line panel. Novel TKI response/resistance markers were identified through cross-analysis of drug response profiles with mutation, gene copy number and expression data. Results All three TKIs were effective against HER2-amplified breast cancer models; neratinib showing the most potent activity, followed by tucatinib then lapatinib. Neratinib displayed the greatest activity in HER2-mutant and EGFR-mutant cells. High expression of HER2, VTCN1, CDK12, and RAC1 correlated with response to all three TKIs. DNA damage repair genes were associated with TKI resistance. BRCA2 mutations were correlated with neratinib and tucatinib response, and high expression of ATM, BRCA2, and BRCA1 were associated with neratinib resistance. Conclusions Neratinib was the most effective HER2-targeted TKI against HER2-amplified, -mutant, and EGFR-mutant cell lines. This analysis revealed novel resistance mechanisms that may be exploited using combinatorial strategies.

This paper provides approximate estimates for the irradiation parameter D10 to globally predict the effectiveness of any irradiation process. D10 is often reported to depend on many specific factors, implying that D10 cannot be estimated without exact knowledge of all factors involved. For specific questions these data can of course be useful but only if the conditions reported exactly match the specific question. Alternatively, this study determined the most relevant factors influencing D10, by quantitatively analyzing data from many references. The best first step appeared to be a classification of the data into vegetative bacteria and spores. As expected, spores were found to have significantly higher D10 values (average 2.48 kGy) than vegetative bacteria (average 0.762 kGy). Further analyses of the vegetative bacteria confirmed the expected extreme irradiation resistance of nonpathogenic Deinococcus radiodurans (average 10.4 kGy). Furthermore the analysis identified Enterococcus faecium, Alcaligenes spp., and several members of the Moraxella–Acinetobacter group as having very high resistance at very low temperatures (average 3.65 kGy). After exclusion of high- and low-resistance spores and some specific conditions showing relevant high or low D10 values, the average for spores was estimated to be 2.11 kGy. For vegetative bacteria this average was estimated to be 0.420 kGy. These approximate estimates are not definite, as they depend on the data used in the analyses. It is expected that inclusion of more data will not change the estimates to a great extent. The approximate estimates are therefore useful tools in designing and evaluating irradiation processes.

Kinase inhibitors form the largest class of precision medicine. From 2013 to 2017, 17 have been approved, with 8 different mechanisms. We present a comprehensive profiling study of all 17 inhibitors on a biochemical assay panel of 280 kinases and proliferation assays of 108 cancer cell lines. Drug responses of the cell lines were related to the presence of frequently recurring point mutations, insertions, deletions, and amplifications in 15 well-known oncogenes and tumor-suppressor genes. In addition, drug responses were correlated with basal gene expression levels with a focus on 383 clinically actionable genes. Cell lines harboring actionable mutations defined in the FDA labels, such as mutant BRAF(V600E) for cobimetinib, or ALK gene translocation for ALK inhibitors, are generally 10 times more sensitive compared with wild-type cell lines. This sensitivity window is more narrow for markers that failed to meet endpoints in clinical trials, for instance CDKN2A loss for CDK4/6 inhibitors (2.7-fold) and KRAS mutation for cobimetinib (2.3-fold). Our data underscore the rationale of a number of recently opened clinical trials, such as ibrutinib in ERBB2-or ERBB4-expressing cancers. We propose and validate new response biomarkers, such as mutation in FBXW7 or SMAD4 for EGFR and HER2 inhibitors, ETV4 and ETV5 expression for MEK inhibitors, and JAK3 expression for ALK inhibitors. Potentially, these new markers could be combined to improve response rates. This comprehensive overview of biochemical and cellular selectivities of approved kinase inhibitor drugs provides a rich resource for drug repurposing, basket trial design, and basic cancer research.