Modelling ligand selectivity of serine proteases using integrative proteochemometric approaches improves model performance and allows the multi-target dependent interpretation of features.

Serine proteases, implicated in important physiological functions, have a high intra-family similarity, which leads to unwanted off-target effects of inhibitors with insufficient selectivity. However, the availability of sequence and structure data has now made it possible to develop approaches to design pharmacological agents that can discriminate successfully between their related binding sites. In this study, we have quantified the relationship between 12,625 distinct protease inhibitors and their bioactivity against 67 targets of the serine protease family (20,213 data points) in an integrative manner, using proteochemometric modelling (PCM). The benchmarking of 21 different target descriptors motivated the usage of specific binding pocket amino acid descriptors, which helped in the identification of active site residues and selective compound chemotypes affecting compound affinity and selectivity. PCM models performed better than alternative approaches (models trained using exclusively compound descriptors on all available data, QSAR) employed for comparison with R(2)/RMSE values of 0.64 ± 0.23/0.66 ± 0.20 vs. 0.35 ± 0.27/1.05 ± 0.27 log units, respectively. Moreover, the interpretation of the PCM model singled out various chemical substructures responsible for bioactivity and selectivity towards particular proteases (thrombin, trypsin and coagulation factor 10) in agreement with the literature. For instance, absence of a tertiary sulphonamide was identified to be responsible for decreased selective activity (by on average 0.27 ± 0.65 pChEMBL units) on FA10. Among the binding pocket residues, the amino acids (arginine, leucine and tyrosine) at positions 35, 39, 60, 93, 140 and 207 were observed as key contributing residues for selective affinity on these three targets.Q.A. thanks the Islamic Development Bank and Cambridge Commonwealth Trust for Funding. O.M.L. is grateful to CONACyT (No. 217442/312933) and the Cambridge Overseas Trust for funding. G.v.W. thanks EMBL 90 (EIPOD) and Marie Curie (COFUND) for funding. A.B. thanks Unilever and the ERC (Starting Grant RC-2013-StG 336159 MIXTURE) for funding. ICC thanks the Institut Pasteur and the Pasteur-Paris International PhD programme for funding. TM thanks the Institut Pasteur for funding.This is the final version of the article. It first appeared from the Royal Society of Chemistry via http://dx.doi.org/10.1039/C4IB00175

Chemically Aware Model Builder (camb): an R package for property and bioactivity modelling of small molecules.

BACKGROUND: In silico predictive models have proved to be valuable for the optimisation of compound potency, selectivity and safety profiles in the drug discovery process. RESULTS: camb is an R package that provides an environment for the rapid generation of quantitative Structure-Property and Structure-Activity models for small molecules (including QSAR, QSPR, QSAM, PCM) and is aimed at both advanced and beginner R users. camb's capabilities include the standardisation of chemical structure representation, computation of 905 one-dimensional and 14 fingerprint type descriptors for small molecules, 8 types of amino acid descriptors, 13 whole protein sequence descriptors, filtering methods for feature selection, generation of predictive models (using an interface to the R package caret), as well as techniques to create model ensembles using techniques from the R package caretEnsemble). Results can be visualised through high-quality, customisable plots (R package ggplot2). CONCLUSIONS: Overall, camb constitutes an open-source framework to perform the following steps: (1) compound standardisation, (2) molecular and protein descriptor calculation, (3) descriptor pre-processing and model training, visualisation and validation, and (4) bioactivity/property prediction for new molecules. camb aims to speed model generation, in order to provide reproducibility and tests of robustness. QSPR and proteochemometric case studies are included which demonstrate camb's application.Graphical abstractFrom compounds and data to models: a complete model building workflow in one package

An upper bound on the Kaon B-parameter and Re(epsilon_K)

New precise data in B physics and theoretical developments in K physics lead us to reconsider the weak K^0-\bar{K}^0 transition from a large-N_c viewpoint, N_c being the number of colors. In this framework, we infer an upper limit on \hat{B}_K and the Kaon indirect CP violation.Comment: 11 pages, 4 figures. V2 : Minor corrections, final version accepted for publication in JHE

Predictive use of the Maximum Entropy Production principle for Past and Present Climates

In this paper, we show how the MEP hypothesis may be used to build simple climate models without representing explicitly the energy transport by the atmosphere. The purpose is twofold. First, we assess the performance of the MEP hypothesis by comparing a simple model with minimal input data to a complex, state-of-the-art General Circulation Model. Next, we show how to improve the realism of MEP climate models by including climate feedbacks, focusing on the case of the water-vapour feedback. We also discuss the dependence of the entropy production rate and predicted surface temperature on the resolution of the model

The GATA1s isoform is normally down-regulated during terminal haematopoietic differentiation and over-expression leads to failure to repress MYB, CCND2 and SKI during erythroid differentiation of K562 cells

Background: Although GATA1 is one of the most extensively studied haematopoietic transcription factors little is currently known about the physiological functions of its naturally occurring isoforms GATA1s and GATA1FL in humans—particularly whether the isoforms have distinct roles in different lineages and whether they have non-redundant roles in haematopoietic differentiation. As well as being of general interest to understanding of haematopoiesis, GATA1 isoform biology is important for children with Down syndrome associated acute megakaryoblastic leukaemia (DS-AMKL) where GATA1FL mutations are an essential driver for disease pathogenesis. <p/>Methods: Human primary cells and cell lines were analyzed using GATA1 isoform specific PCR. K562 cells expressing GATA1s or GATA1FL transgenes were used to model the effects of the two isoforms on in vitro haematopoietic differentiation. <p/>Results: We found no evidence for lineage specific use of GATA1 isoforms; however GATA1s transcripts, but not GATA1FL transcripts, are down-regulated during in vitro induction of terminal megakaryocytic and erythroid differentiation in the cell line K562. In addition, transgenic K562-GATA1s and K562-GATA1FL cells have distinct gene expression profiles both in steady state and during terminal erythroid differentiation, with GATA1s expression characterised by lack of repression of MYB, CCND2 and SKI. <p/>Conclusions: These findings support the theory that the GATA1s isoform plays a role in the maintenance of proliferative multipotent megakaryocyte-erythroid precursor cells and must be down-regulated prior to terminal differentiation. In addition our data suggest that SKI may be a potential therapeutic target for the treatment of children with DS-AMKL

The predictive and prognostic potential of plasma telomerase reverse transcriptase (TERT) RNA in rectal cancer patients

Background: Preoperative chemoradiotherapy (CRT) followed by surgery is the standard care for locally advanced rectal cancer, but tumour response to CRT and disease outcome are variable. The current study aimed to investigate the effectiveness of plasma telomerase reverse transcriptase (TERT) levels in predicting tumour response and clinical outcome. Methods: 176 rectal cancer patients were included. Plasma samples were collected at baseline (before CRT\ubcT0), 2 weeks after CRT was initiated (T1), post-CRT and before surgery (T2), and 4\u20138 months after surgery (T3) time points. Plasma TERT mRNA levels and total cell-free RNA were determined using real-time PCR. Results: Plasma levels of TERT were significantly lower at T2 (Po0.0001) in responders than in non-responders. Post-CRT TERT levels and the differences between pre- and post-CRT TERT levels independently predicted tumour response, and the prediction model had an area under curve of 0.80 (95% confidence interval (CI) 0.73\u20130.87). Multiple analysis demonstrated that patients with detectable TERT levels at T2 and T3 time points had a risk of disease progression 2.13 (95% CI 1.10\u20134.11)-fold and 4.55 (95% CI 1.48\u201313.95)-fold higher, respectively, than those with undetectable plasma TERT levels. Conclusions: Plasma TERT levels are independent markers of tumour response and are prognostic of disease progression in rectal cancer patients who undergo neoadjuvant therapy

Luminally expressed gastrointestinal biomarkers

Introduction: A biomarker is a measurable indicator of normal biologic processes, pathogenic processes or pharmacological responses. The identification of a useful biomarker is challenging, with several hurdles to overcome before clinical adoption. This review gives a general overview of a range of biomarkers associated with inflammatory bowel disease or colorectal cancer along the gastrointestinal tract. Areas covered: These markers include those that are already clinically accepted, such as inflammatory markers such as faecal calprotectin, S100A12 (Calgranulin C), Fatty Acid Binding Proteins (FABP), malignancy markers such as Faecal Occult Blood, Mucins, Stool DNA, Faecal microRNA (miRNA), other markers such as Faecal Elastase, Faecal alpha-1-antitrypsin, Alpha2-macroglobulin and possible future markers such as microbiota, volatile organic compounds and pH. Expert commentary: There are currently a few biomarkers that have been sufficiently validated for routine clinical use at present such as FC. However, many of these biomarkers continue to be limited in sensitivity and specificity for various GI diseases. Emerging biomarkers have the potential to improve diagnosis and monitoring but further study is required to determine efficacy and validate clinical utility

International Lessons in New Methods for Grading and Integrating Cost Effectiveness Evidence into Clinical Practice Guidelines

Economic evidence is influential in health technology assessment world-wide. Clinical Practice Guidelines (CPG) can enable economists to include economic information on health care provision. Application of economic evidence in CPGs, and its integration into clinical practice and national decision making is hampered by objections from professions, paucity of economic evidence or lack of policy commitment. The use of state-of-art economic methodologies will improve this. Economic evidence can be graded by 'checklists' to establish the best evidence for decision making given methodological rigor. New economic evaluation checklists, Multi-Criteria Decision Analyses (MCDA) and other decision criteria enable health economists to impact on decision making world-wide. We analyse the methodologies for integrating economic evidence into CPG agencies globally, including the Agency of Health Research and Quality (AHRQ) in the USA, National Health and Medical Research Council (NHMRC) and Australian political reforms. The Guidelines and Economists Network International (GENI) Board members from Australia, UK, Canada and Denmark presented the findings at the conference of the International Health Economists Association (IHEA) and we report conclusions and developments since. The Consolidated Guidelines for the Reporting of Economic Evaluations (CHEERS) 24 item check list can be used by AHRQ, NHMRC, other CPG and health organisations, in conjunction with the Drummond ten-point check list and a questionnaire that scores that checklist for grading studies, when assessing economic evidence. Cost-effectiveness Analysis (CEA) thresholds, opportunity cost and willingness-to-pay (WTP) are crucial issues for decision rules in CEA generally, including end-of-life therapies. Limitations of inter-rater reliability in checklists can be addressed by including more than one assessor to reach a consensus, especially when impacting on treatment decisions. We identify priority areas to generate economic evidence for CPGs by NHMRC, AHRQ, and other agencies. The evidence may cover demand for care issues such as involved time, logistics, innovation price, price sensitivity, substitutes and complements, WTP, absenteeism and presentism. Supply issues may include economies of scale, efficiency changes, and return on investment. Involved equity and efficiency measures may include cost-of-illness, disease burden, quality-of-life, budget impact, cost-effective ratios, net benefits and disparities in access and outcomes.. Priority setting remains essential and trade-off decisions between policy criteria can be based on MCDA, both in evidence based clinical medicine and in health planning