Relativistic DFT calculation and their effect on the accuracy of results

This study explores the significance of density functional theory (DFT) calculations with relativistic effects for two ethylenediaminetetraacetate (edta) type complexes: trans(O5)-[M(eddadp)]- (M = Rh3+, Co3+). Relativistic effects affect the electronic structure of a molecule and, thus, its chemical and spectroscopic properties. With the use of scalar relativistic corrections (SR-ZORA), as implemented in the ADF package, with the B3LYP functional, the TZP basis set and the COSMO solvation model, structural analyses show improved predictions for the geometries of both complexes. In the case of the Rh3+ complex, the differences in metal-ligand bond lengths with and without the relativistic effects were small. In the case of the Co3+ complex, the changes in metal-ligand bond lengths due to the relativistic effects were slightly more pronounced. Compared to experimental values, excitation energies are better when including relativistic corrections, especially for the Rh3+ complex. These results indicate the importance of relativistic DFT calculations for heavy element compounds

Development of a treatment selection algorithm for SGLT2 and DPP-4 inhibitor therapies in people with type 2 diabetes: a retrospective cohort study

This is the final version. Available from Elsevier via the DOI in this record. Data sharing: CPRD data are available by application to the CPRD Independent Scientific Advisory Committee and clinical trial data are accessible by application to the Yale University Open Data Access Project and Vivli.Background Current treatment guidelines do not provide recommendations to support the selection of treatment for most people with type 2 diabetes. We aimed to develop and validate an algorithm to allow selection of optimal treatment based on glycaemic response, weight change, and tolerability outcomes when choosing between SGLT2 inhibitor or DPP-4 inhibitor therapies. Methods In this retrospective cohort study, we identified patients initiating SGLT2 and DPP-4 inhibitor therapies after Jan 1, 2013, from the UK Clinical Practice Research Datalink (CPRD). We excluded those who received SGLT2 or DPP-4 inhibitors as first-line treatment or insulin at the same time, had estimated glomerular filtration rate (eGFR) of less than 45 mL/min per 1·73 m2, or did not have a valid baseline glycated haemoglobin (HbA1c) measure (<53 or ≥120 mmol/mol). The primary efficacy outcome was the HbA1c value reached 6 months after drug initiation, adjusted for baseline HbA1c. Clinical features associated with differential HbA1c outcome on the two therapies were identified in CPRD (n=26 877), and replicated in reanalysis of 14 clinical trials (n=10 414). An algorithm to predict individual-level differential HbA1c outcome on the two therapies was developed in CPRD (derivation; n=14 069) and validated in head-to-head trials (n=2499) and CPRD (independent validation; n=9376). In CPRD, we further explored heterogeneity in 6-month weight change and treatment discontinuation. Findings Among 10 253 patients initiating SGLT2 inhibitors and 16 624 patients initiating DPP-4 inhibitors in CPRD, baseline HbA1c, age, BMI, eGFR, and alanine aminotransferase were associated with differential HbA1c outcome with SGLT2 inhibitor and DPP-4 inhibitor therapies. The median age of participants was 62·0 years (IQR 55·0–70·0). 10 016 (37·3%) were women and 16 861 (62·7%) were men. An algorithm based on these five features identified a subgroup, representing around four in ten CPRD patients, with a 5 mmol/mol or greater observed benefit with SGLT2 inhibitors in all validation cohorts (CPRD 8·8 mmol/mol [95% CI 7·8–9·8]; CANTATA-D and CANTATA-D2 trials 5·8 mmol/mol [3·9–7·7]; BI1245.20 trial 6·6 mmol/mol [2·2–11·0]). In CPRD, predicted differential HbA1c response with SGLT2 inhibitor and DPP-4 inhibitor therapies was not associated with weight change. Overall treatment discontinuation within 6 months was similar in patients predicted to have an HbA1c benefit with SGLT2 inhibitors over DPP-4 inhibitors (median 15·2% [13·2–20·3] vs 14·4% [12·9–16·7]). A smaller subgroup predicted to have greater HbA1c reduction with DPP-4 inhibitors were twice as likely to discontinue SGLT2 inhibitors than DPP-4 inhibitors (median 26·8% [23·4–31·0] vs 14·8% [12·9–16·8]). Interpretation A validated treatment selection algorithm for SGLT2 inhibitor and DPP-4 inhibitor therapies can support decisions on optimal treatment for people with type 2 diabetes.BHF-Turing Cardiovascular Data Science AwardMedical Research Counci

Online tools to support teaching and training activities in chemical engineering: enzymatic proteolysis

The practical teaching or training of enzymatic proteolysis can prove challenging because of the lengthy duration of the process, the complexity of identifying short amino acid sequences, the high cost of the enzymes, and the need to use very specific equipment. There are several freely-available online tools that, despite being employed by scientists to help identify bioactive peptides, are not commonly used for teaching and training activities. This work summarises the most common protein and peptide databases along with other tools that allow one to simulate enzymatic hydrolysis of a given protein and to study the structure, physicochemical properties, bioactivity, toxicity, allergenicity, and even the bitterness of the resulting peptides. Overall, in silico tools can be used during the teaching and training of chemical engineers as innovative alternatives to conventional laboratory work and theoretical classes

Developing clinical prediction models for diabetes classification and progression

Patients with type 1 and type 2 diabetes have very different treatment and care requirements. Overlapping phenotypes and lack of clear classification guidelines make it difficult for clinicians to differentiate between type 1 and type 2 diabetes at diagnosis. The rate of glycaemic deterioration is highly variable in patients with type 2 diabetes but there is no single test to accurately identify which patients will progress rapidly to requiring insulin therapy. Incorrect treatment and care decisions in diabetes can have life-threatening consequences. The aim of this thesis is to develop clinical prediction models that can be incorporated into routine clinical practice to assist clinicians with the classification and care of patient diagnosed with diabetes. We addressed the problem first by integrating features previously associated with classification of type 1 and type 2 diabetes to develop a diagnostic model using logistic regression to identify, at diagnosis, patients with type 1 diabetes. The high performance achieved by this model was comparable to that of machine learning algorithms. In patients diagnosed with type 2 diabetes, we found that patients who were GADA positive and had genetic susceptibility to type 1 diabetes progressed more rapidly to requiring insulin therapy. We built upon this finding to develop a prognostic model integrating predictive features of glycaemic deterioration to predict early insulin requirement in adults diagnosed with type 2 diabetes. The three main findings of this thesis have the potential to change the way that patients with diabetes are managed in clinical practice. Use of the diagnostic model developed to identify patients with type 1 diabetes has the potential to reduce misclassification. Classifying patients according to the model has the benefit of being more akin to the treatment needs of the patient rather than the aetiopathological definitions used in current clinical guidelines. The design of the model lends itself to implementing a triage-based approach to diabetes subtype diagnosis. Our second main finding alters the clinical implications of a positive GADA test in patients diagnosed with type 2 diabetes. For identifying patients likely to progress rapidly to insulin, genetic testing is only beneficial in patients who test positive for GADA. In clinical practice, a two-step screening process could be implemented - only patients who test positive for GADA in the first step would go on for genetic testing. The prognostic model can be used in clinical practice to predict a patient’s rate of glycaemic deterioration leading to a requirement for insulin. The availability of this data will enable clinical practices to more effectively manage their patient lists, prioritising more intensive follow up for those patients who are at high risk of rapid progression. Patients are likely to benefit from tailored treatment. Another key clinical use of the prognostic model is the identification of patients who would benefit most from GADA testing saving both inconvenience to the patient and a cost-benefit to the health service

Probing the potential of bioactive compounds of millets as an inhibitor for lifestyle diseases: molecular docking and simulation-based approach

Millets are becoming more popular as a healthy substitute for people with lifestyle disorders. They offer dietary fiber, polyphenols, fatty acids, minerals, vitamins, protein, and antioxidants. The nutritional importance of millets leads to the present in-silico study of selective bioactive compounds docked against the targets of lifestyle diseases, viz., diabetes, hypertension, and atherosclerosis using molecular docking and molecular simulations approach. Pharmacokinetic analysis was also carried out to analyse ADME properties and toxicity analysis, drug-likeliness, and finally target prediction for new targets for uncharacterized compounds or secondary targets for recognized molecules by Swiss Target Prediction was also done. The docking results revealed that the bioactive compound flavan-4-ol, among all the 50 compounds studied, best docked to all the four targets of lifestyle diseases, viz., Human dipeptidyl peptidase IV (−5.94 kcal mol−1 binding energy), Sodium-glucose cotransporter-2 (−6.49 kcal mol−1) diabetes-related enzyme, the Human angiotensin-converting enzyme (−6.31 kcal mol−1) which plays a significant role in hypertension, and Proprotein convertase subtilisin kexin type 9 (−4.67 kcal mol−1) for atherosclerosis. Molecular dynamics simulation analysis substantiates that the flavan-4-ol forms a better stability complex with all the targets. ADMET profiles further strengthened the candidature of the flavan-4-ol bioactive compound to be considered for trial as an inhibitor of targets DPPIV, SGLT2, PCSK9, and hACE. We suggest that more research be conducted, taking Flavon-4-ol into account where it can be used as standard treatment for lifestyle diseases