Mathematical modelling of immune condition dynamics : a clinical perspective

This thesis describes the use of mathematical modelling to analyse the treatment of patients with immune disorders; namely, Multiple Myeloma, a cancer of plasma cells that create excess monoclonal antibody; and kidney transplants, where the immune system produces polygonal antibodies against the implanted organ. Linear and nonlinear compartmental models play an important role in the analysis of biomedical systems; in this thesis several models are developed to describe the in vivo kinetics of the antibodies that are prevalent for the two disorders studied. These models are validated against patient data supplied by clinical collaborators. Through this validation process important information regarding the dynamic properties of the clinical treatment can be gathered. In order to treat patients with excess immune antibodies the clinical staff wish to reduce these high levels in the patient to near healthy concentrations. To achieve this they have two possible treatment modalities: either using artificial methods to clear the material, a process known as apheresis, or drug therapy to reduce the production of the antibody in question. Apheresis techniques differ in their ability to clear different immune complexes; the effectiveness of a range of apheresis techniques is categorised for several antibody types and antibody fragments. The models developed are then used to predict the patient response to alternative treatment methods, and schedules, to find optimal combinations. In addition, improved measurement techniques that may offer an improved diagnosis are suggested. Whilst the overall effect of drug therapy is known, through measuring the concentration of antibodies in the patient’s blood, the short-term relationship between drug application and reduction in antibody synthesis is still not well defined; therefore, methods to estimate the generation rate of the immune complex, without the need for invasive procedures, are also presented

Mathematical modelling of immune condition dynamics : a clinical perspective

This thesis describes the use of mathematical modelling to analyse the treatment of patients with immune disorders; namely, Multiple Myeloma, a cancer of plasma cells that create excess monoclonal antibody; and kidney transplants, where the immune system produces polygonal antibodies against the implanted organ. Linear and nonlinear compartmental models play an important role in the analysis of biomedical systems; in this thesis several models are developed to describe the in vivo kinetics of the antibodies that are prevalent for the two disorders studied. These models are validated against patient data supplied by clinical collaborators. Through this validation process important information regarding the dynamic properties of the clinical treatment can be gathered. In order to treat patients with excess immune antibodies the clinical staff wish to reduce these high levels in the patient to near healthy concentrations. To achieve this they have two possible treatment modalities: either using artificial methods to clear the material, a process known as apheresis, or drug therapy to reduce the production of the antibody in question. Apheresis techniques differ in their ability to clear different immune complexes; the effectiveness of a range of apheresis techniques is categorised for several antibody types and antibody fragments. The models developed are then used to predict the patient response to alternative treatment methods, and schedules, to find optimal combinations. In addition, improved measurement techniques that may offer an improved diagnosis are suggested. Whilst the overall effect of drug therapy is known, through measuring the concentration of antibodies in the patient’s blood, the short-term relationship between drug application and reduction in antibody synthesis is still not well defined; therefore, methods to estimate the generation rate of the immune complex, without the need for invasive procedures, are also presented.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Evaluating associations between the benefits and risks of drug therapy in type 2 diabetes:A joint modelling approach

This is the author accepted manuscript. The final version is available from Dove Medical Press via the DOI in this record.Data statement: No additional data are available from the authors although the individual participant data from the ADOPT trial used in this study are available from GlaxoSmithKline on application via www.clinicalstudydatarequest.comObjective: Precision medicine drug therapy seeks to maximise efficacy and minimise harm for individual patients. This will be difficult if drug response and side-effects are positively associated, meaning patients likely to respond best are at increased risk of side-effects. We applied joint longitudinal-survival models to evaluate associations between drug response (longitudinal outcome) and risk of side-effects (survival outcome) for patients initiating type 2 diabetes therapy. Study Design and Setting: Participants were randomised to metformin, sulfonylurea or thiazolidinedione therapy in the ADOPT drug-efficacy trial (n=4,351). Joint models were parameterised for: 1) current HbA1c response (change from baseline in HbA1c); 2) cumulative HbA1c response (total HbA1c change). Results: With metformin, greater HbA1c response did not increase risk of gastrointestinal events (Hazard ratio (HR) per 1% absolute greater current response 0.82 (95% confidence interval 0.67,1.01); HR per 1% higher cumulative response 0.90 (0.81,1.00)). With sulfonylureas, greater current response was associated with increased risk of hypoglycaemia (HR 1.41 (1.04,1.91)). With thiazolidinediones, greater response was associated with increased risk of oedema (current HR 1.45 (1.05,2.01); cumulative 1.22 (1.07,1.38)) but not fracture. Conclusion: Joint modelling provides a useful framework to evaluate the association between response to a drug and risk of developing side-effects. There may be great potential for widespread application of joint modelling to evaluate the risks and benefits of both new and established medications.This work was supported by the Medical Research Council (UK) (Grant MR/N00633X/1). ATH is a NIHR Senior Investigator and a Wellcome Trust Senior Investigator. ERP is a Wellcome Trust New Investigator (102820/Z/13/Z). AGJ is supported by an NIHR Clinician Scientist award. ATH and BMS are supported by the NIHR Exeter Clinical Research Facility. WEH received additional support from IQVIA and the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care South West Peninsula (NIHR CLAHRC South West Peninsula)

Comment on “minimal and maximal models to quantitate glucose metabolism : tools to measure, to simulate and to run in silico clinical trials"

Comment on “Minimal and Maximal Models to Quantitate Glucose Metabolism: Tools to Measure, to Simulate and to Run in Silico Clinical Trials

Bayesian parameter estimation in the oral minimal model of glucose dynamics from non-fasting conditions using a new function of glucose appearance

Background and objective The oral minimal model (OMM) of glucose dynamics is a prominent method for assessing postprandial glucose metabolism. The model yields estimates of insulin sensitivity and the meal-related appearance of glucose from insulin and glucose data after an oral glucose challenge. Despite its success, the OMM approach has several weaknesses that this paper addresses. Methods A novel procedure introducing three methodological adaptations to the OMM approach is proposed. These are: (1) the use of a fully Bayesian and efficient method for parameter estimation, (2) the model identification from non-fasting conditions using a generalised model formulation and (3) the introduction of a novel function to represent the meal-related glucose appearance based on two superimposed components utilising a modified structure of the log-normal distribution. The proposed modelling procedure is applied to glucose and insulin data from subjects with normal glucose tolerance consuming three consecutive meals in intervals of four hours. Results It is shown that the glucose effectiveness parameter of the OMM is, contrary to previous results, structurally globally identifiable. In comparison to results from existing studies that use the conventional identification procedure, the proposed approach yields an equivalent level of model fit and a similar precision of insulin sensitivity estimates. Furthermore, the new procedure shows no deterioration of model fit when data from non-fasting conditions are used. In comparison to the conventional, piecewise linear function of glucose appearance, the novel log-normally based function provides an improved model fit in the first 30 min of the response and thus a more realistic estimation of glucose appearance during this period. The identification procedure is implemented in freely accesible MATLAB and Python software packages. Conclusions We propose an improved and freely available method for the identification of the OMM which could become the future standardard for the oral minimal modelling method of glucose dynamics

A glucose-only model to extract physiological information from postprandial glucose profiles in subjects with normal glucose tolerance

Background: Current mathematical models of postprandial glucose metabolism in people with normal and impaired glucose tolerance rely on insulin measurements and are therefore not applicable in clinical practice. This research aims to develop a model that only requires glucose data for parameter estimation while also providing useful information on insulin sensitivity, insulin dynamics and the meal-related glucose appearance (GA). Methods: The proposed glucose-only model (GOM) is based on the oral minimal model (OMM) of glucose dynamics and substitutes the insulin dynamics with a novel function dependant on glucose levels and GA. A Bayesian method and glucose data from 22 subjects with normal glucose tolerance are utilised for parameter estimation. To validate the results of the GOM, a comparison to the results of the OMM, obtained by using glucose and insulin data from the same subjects is carried out. Results: The proposed GOM describes the glucose dynamics with comparable precision to the OMM with an RMSE of 5.1 ± 2.3 mg/dL and 5.3 ± 2.4 mg/dL, respectively and contains a parameter that is significantly correlated to the insulin sensitivity estimated by the OMM (r = 0.7) Furthermore, the dynamic properties of the time profiles of GA and insulin dynamics inferred by the GOM show high similarity to the corresponding results of the OMM. Conclusions: The proposed GOM can be used to extract useful physiological information on glucose metabolism in subjects with normal glucose tolerance. The model can be further developed for clinical applications to patients with impaired glucose tolerance under the use of continuous glucose monitoring data

Metabolic phenotype of male obesity-related secondary hypogonadism pre-replacementand post-replacement therapy with intra-muscular testosterone undecanoate therapy

Aim: To explore the metabolic phenotype of obesity-related Secondary Hypogonadism (SH) in men pre- and post-replacement therapy with long-acting intramuscular (IM) testosterone undecanoate (TU). Methods: A prospective observational pilot study on metabolic effects of TU IM in male obesity-related SH (Hypogonadal [HG] group, n=13), including baseline comparisons with controls (Eugonadal [EG] group, n=15). Half the subjects (n=7 in each group) had Type 2 Diabetes Mellitus (T2D). Baseline metabolic assessment on Human Metabolism Research Unit: fasting blood samples; BodPod (body composition), and; whole-body indirect calorimetry. The HG group was treated with TU IM therapy for 6-29 months (mean 14.8-months [SD 8.7]), and assessment at the Human Metabolism Research Unit repeated. T-test comparisons were performed between baseline and follow-up data (HG group), and between baseline data (HG and EG groups). Data reported as mean (SD). Results: Overall, TU IM therapy resulted in a statistically significant improvement in HbA1C (9mmol/mol, P=0.03), with 52% improvement in HOMA%B. Improvement in glycaemic control was driven by the HG subgroup with T2D, with 18mmol/mol [P=0.02] improvement in HbA1C. Following TU IM therapy, there was a statistically significant reduction in fat mass (3.5Kg, P=0.03) and increase in lean body mass (2.9Kg, P=0.03). Lipid profiles and energy expenditure were unchanged following TU IM therapy. Comparisons between baseline data for HG and EG groups were equivalent apart from differences in testosterone, SHBG and BMR. Conclusion: In men with obesity-related SH (including a subgroup with T2D), TU IM therapy improved glycaemic control, beta cell function and body composition

Safety and effectiveness of SGLT2 inhibitors in a UK population with type 2 diabetes and aged over 70 years:an instrumental variable approach

Aims/hypothesis: Older adults are under-represented in trials, meaning the benefits and risks of glucose-lowering agents in this age group are unclear. The aim of this study was to assess the safety and effectiveness of sodium–glucose cotransporter 2 inhibitors (SGLT2i) in people with type 2 diabetes aged over 70 years using causal analysis. Methods: Hospital-linked UK primary care data (Clinical Practice Research Datalink, 2013–2020) were used to compare adverse events and effectiveness in individuals initiating SGLT2i compared with dipeptidyl peptidase-4 inhibitors (DPP4i). Analysis was age-stratified: &lt;70 years (SGLT2i n=66,810, DPP4i n=76,172), ≥70 years (SGLT2i n=10,419, DPP4i n=33,434). Outcomes were assessed using the instrumental variable causal inference method and prescriber preference as the instrument. Results: Risk of diabetic ketoacidosis was increased with SGLT2i in those aged ≥70 (incidence rate ratio compared with DPP4i: 3.82 [95% CI 1.12, 13.03]), but not in those aged &lt;70 (1.12 [0.41, 3.04]). However, incidence rates with SGLT2i in those ≥70 was low (29.6 [29.5, 29.7]) per 10,000 person-years. SGLT2i were associated with similarly increased risk of genital infection in both age groups (incidence rate ratio in those &lt;70: 2.27 [2.03, 2.53]; ≥70: 2.16 [1.77, 2.63]). There was no evidence of an increased risk of volume depletion, poor micturition control, urinary frequency, falls or amputation with SGLT2i in either age group. In those ≥70, HbA1c reduction was similar between SGLT2i and DPP4i (−0.3 mmol/mol [−1.6, 1.1], −0.02% [0.1, 0.1]), but in those &lt;70, SGLT2i were more effective (−4 mmol/mol [4.8, −3.1], −0.4% [−0.4, −0.3]). Conclusions/interpretation: Causal analysis suggests SGLT2i are effective in adults aged ≥70 years, but increase risk for genital infections and diabetic ketoacidosis. Our study extends RCT evidence to older adults with type 2 diabetes.</p

Screening for malnutrition in patients with gastro-entero-pancreatic neuroendocrine tumours : a cross-sectional study

Objectives To investigate whether screening for malnutrition using the validated malnutrition universal screening tool (MUST) identifies specific characteristics of patients at risk, in patients with gastro-entero-pancreatic neuroendocrine tumours (GEP-NET). Design Cross-sectional study. Setting University Hospitals Coventry & Warwickshire NHS Trust; European Neuroendocrine Tumour Society Centre of Excellence. Participants Patients with confirmed GEP-NET (n=161) of varying primary tumour sites, functioning status, grading, staging and treatment modalities. Main outcome measure To identify disease and treatment-related characteristics of patients with GEP-NET who score using MUST, and should be directed to detailed nutritional assessment. Results MUST score was positive (≥1) in 14% of outpatients with GEP-NET. MUST-positive patients had lower faecal elastase concentrations compared to MUST-negative patients (244±37 vs 383±20 µg/g stool; p=0.018), and were more likely to be on treatment with long-acting somatostatin analogues (65 vs 38%, p=0.021). MUST-positive patients were also more likely to have rectal or unknown primary NET, whereas, frequencies of other GEP-NET including pancreatic NET were comparable between MUST-positive and MUST-negative patients. Conclusions Given the frequency of patients identified at malnutrition risk using MUST in our relatively large and diverse GEP-NET cohort and the clinical implications of detecting malnutrition early, we recommend routine use of malnutrition screening in all patients with GEP-NET, and particularly in patients who are treated with long-acting somatostatin analogues

Time trends in prescribing of type 2 diabetes drugs, glycaemic response and risk factors:a retrospective analysis of primary care data, 2010-2017

This is the author accepted manuscript. The final version is available on open access from Wiley via the DOI in this recordAim: Prescribing in type 2 diabetes has changed markedly in recent years, with increasing use of newer, more expensive glucose-lowering drugs. We aimed to describe population-level time trends in both prescribing patterns and short-term patient outcomes (HbA1c, weight, blood pressure, hypoglycemia and treatment discontinuation) after initiating new therapy. Materials and methods: We studied 81,532 UK patients with type 2 diabetes initiating a first to fourth line drug in primary care between 2010-2017 inclusive (Clinical Practice Research Datalink). Trends in new prescriptions and subsequent six and twelve-month adjusted changes in glycemic response (reduction in HbA1c), weight, blood pressure, and rates of hypoglycemia and treatment discontinuation were examined. Results: DPP4-inhibitor use second-line near doubled (41% of new prescriptions in 2017 vs. 22% 2010), replacing sulfonylureas as the most common second-line drug (29% 2017 vs. 53% 2010). SGLT2-inhibitors, introduced in 2013, comprised 17% of new first-fourth line prescriptions by 2017. First-line use of metformin remained stable (91% of new prescriptions in 2017 vs. 91% 2010). Over the study period there was little change in average glycemic response and treatment discontinuation. There was a modest reduction in weight second and third-line (second line 2017 vs. 2010: -1.5 kg (95%CI -1.9;-1.1), p<0.001), and a slight reduction in systolic blood pressure first to third-line (2017 vs. 2010 difference range -1.7 to -2.1 mmHg, all p<0.001). Hypoglycemia rates decreased second-line (incidence rate ratio 0.94 per-year (95%CI 0.88;1.00, p=0.04)), mirroring the decline in use of sulfonylureas. 4 Conclusions: Recent changes in prescribing of therapy in type 2 diabetes have not led to a change in glycemic response and have resulted in modest improvements in other population-level short-term patient outcomes.Medical Research Council (MRC)National Institute for Health Research (NIHR)Wellcome Trus