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Franks PW, Cefalu WT, Dennis J, Florez JC, Mathieu C, Morton RW, Ridderstråle M, Sillesen HH, Stehouwer CDA. Precision medicine for cardiometabolic disease: a framework for clinical translation. Lancet Diabetes Endocrinol 2023; 11:822-835. [PMID: 37804856 DOI: 10.1016/s2213-8587(23)00165-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 06/01/2023] [Accepted: 06/01/2023] [Indexed: 10/09/2023]
Abstract
Cardiometabolic disease is a major threat to global health. Precision medicine has great potential to help to reduce the burden of this common and complex disease cluster, and to enhance contemporary evidence-based medicine. Its key pillars are diagnostics; prediction (of the primary disease); prevention (of the primary disease); prognosis (prediction of complications of the primary disease); treatment (of the primary disease or its complications); and monitoring (of risk exposure, treatment response, and disease progression or remission). To contextualise precision medicine in both research and clinical settings, and to encourage the successful translation of discovery science into clinical practice, in this Series paper we outline a model (the EPPOS model) that builds on contemporary evidence-based approaches; includes precision medicine that improves disease-related predictions by stratifying a cohort into subgroups of similar characteristics, or using participants' characteristics to model treatment outcomes directly; includes personalised medicine with the use of a person's data to objectively gauge the efficacy, safety, and tolerability of therapeutics; and subjectively tailors medical decisions to the individual's preferences, circumstances, and capabilities. Precision medicine requires a well functioning system comprised of multiple stakeholders, including health-care recipients, health-care providers, scientists, health economists, funders, innovators of medicines and technologies, regulators, and policy makers. Powerful computing infrastructures supporting appropriate analysis of large-scale, well curated, and accessible health databases that contain high-quality, multidimensional, time-series data will be required; so too will prospective cohort studies in diverse populations designed to generate novel hypotheses, and clinical trials designed to test them. Here, we carefully consider these topics and describe a framework for the integration of precision medicine in cardiometabolic disease.
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Affiliation(s)
- Paul W Franks
- Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark; Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden; Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Harvard T H Chan School of Public Health, Boston, MA, USA.
| | - William T Cefalu
- Division of Diabetes, Endocrinology and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - John Dennis
- Institute of Biomedical and Clinical Science, Royal Devon and Exeter Hospital, University of Exeter, Exeter, UK
| | - Jose C Florez
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Chantal Mathieu
- Clinical and Experimental Endocrinology, UZ Gasthuisberg, KU Leuven, Leuven, Belgium
| | - Robert W Morton
- Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark
| | | | - Henrik H Sillesen
- Department of Clinical Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark
| | - Coen D A Stehouwer
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands; Department of Internal Medicine, Maastricht University Medical Centre, Maastricht, Netherlands
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Misra S, Aguilar-Salinas CA, Chikowore T, Konradsen F, Ma RCW, Mbau L, Mohan V, Morton RW, Nyirenda MJ, Tapela N, Franks PW. The case for precision medicine in the prevention, diagnosis, and treatment of cardiometabolic diseases in low-income and middle-income countries. Lancet Diabetes Endocrinol 2023; 11:836-847. [PMID: 37804857 DOI: 10.1016/s2213-8587(23)00164-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 05/08/2023] [Accepted: 06/01/2023] [Indexed: 10/09/2023]
Abstract
Cardiometabolic diseases are the leading preventable causes of death in most geographies. The causes, clinical presentations, and pathogenesis of cardiometabolic diseases vary greatly worldwide, as do the resources and strategies needed to prevent and treat them. Therefore, there is no single solution and health care should be optimised, if not to the individual (ie, personalised health care), then at least to population subgroups (ie, precision medicine). This optimisation should involve tailoring health care to individual disease characteristics according to ethnicity, biology, behaviour, environment, and subjective person-level characteristics. The capacity and availability of local resources and infrastructures should also be considered. Evidence needed for equitable precision medicine cannot be generated without adequate data from all target populations, and the idea that research done in high-income countries will transfer adequately to low-income and middle-income countries (LMICs) is problematic, as many migration studies and transethnic comparisons have shown. However, most data for precision medicine research are derived from people of European ancestry living in high-income countries. In this Series paper, we discuss the case for precision medicine for cardiometabolic diseases in LMICs, the barriers and enablers, and key considerations for implementation. We focus on three propositions: first, failure to explore and implement precision medicine for cardiometabolic disease in LMICs will enhance global health disparities. Second, some LMICs might already be placed to implement cardiometabolic precision medicine under appropriate circumstances, owing to progress made in treating infectious diseases. Third, improvements in population health from precision medicine are most probably asymptotic; the greatest gains are more likely to be obtained in countries where health-care systems are less developed. We outline key recommendations for implementation of precision medicine approaches in LMICs.
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Affiliation(s)
- Shivani Misra
- Division of Metabolism, Digestion and Reproduction, Imperial College London, London, UK; Department of Diabetes and Endocrinology, St Mary's Hospital, Imperial College Healthcare NHS Trust, London, UK
| | - Carlos A Aguilar-Salinas
- Dirección de Nutricion, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico; Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, México
| | - Tinashe Chikowore
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Flemming Konradsen
- Novo Nordisk Foundation, Copenhagen, Denmark; Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Ronald C W Ma
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Li Ka Shing Institute of Health Sciences, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | | | - Viswanathan Mohan
- Madras Diabetes Research Foundation, ICMR Centre for Advanced Research in Diabetes, Chennai, India; Dr Mohan's Diabetes Specialties Centre, IDF Centre of Excellence in Diabetes Care, Chennai, India
| | | | - Moffat J Nyirenda
- MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda; London School of Hygiene and Tropical Medicine, London, UK
| | - Neo Tapela
- Botswana Harvard AIDS Institute Partnership, Gaborone, Botswana; International Consortium for Health Outcomes Measurement, Oxford, UK
| | - Paul W Franks
- Novo Nordisk Foundation, Copenhagen, Denmark; Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden; Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Harvard T H Chan School of Public Health, Boston, MA, USA.
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Shields BM, Dennis JM, Angwin CD, Warren F, Henley WE, Farmer AJ, Sattar N, Holman RR, Jones AG, Pearson ER, Hattersley AT. Patient stratification for determining optimal second-line and third-line therapy for type 2 diabetes: the TriMaster study. Nat Med 2023; 29:376-383. [PMID: 36477733 PMCID: PMC7614216 DOI: 10.1038/s41591-022-02120-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 11/07/2022] [Indexed: 12/13/2022]
Abstract
Precision medicine aims to treat an individual based on their clinical characteristics. A differential drug response, critical to using these features for therapy selection, has never been examined directly in type 2 diabetes. In this study, we tested two hypotheses: (1) individuals with body mass index (BMI) > 30 kg/m2, compared to BMI ≤ 30 kg/m2, have greater glucose lowering with thiazolidinediones than with DPP4 inhibitors, and (2) individuals with estimated glomerular filtration rate (eGFR) 60-90 ml/min/1.73 m2, compared to eGFR >90 ml/min/1.73 m2, have greater glucose lowering with DPP4 inhibitors than with SGLT2 inhibitors. The primary endpoint for both hypotheses was the achieved HbA1c difference between strata for the two drugs. In total, 525 people with type 2 diabetes participated in this UK-based randomized, double-blind, three-way crossover trial of 16 weeks of treatment with each of sitagliptin 100 mg once daily, canagliflozin 100 mg once daily and pioglitazone 30 mg once daily added to metformin alone or metformin plus sulfonylurea. Overall, the achieved HbA1c was similar for the three drugs: pioglitazone 59.6 mmol/mol, sitagliptin 60.0 mmol/mol and canagliflozin 60.6 mmol/mol (P = 0.2). Participants with BMI > 30 kg/m2, compared to BMI ≤ 30 kg/m2, had a 2.88 mmol/mol (95% confidence interval (CI): 0.98, 4.79) lower HbA1c on pioglitazone than on sitagliptin (n = 356, P = 0.003). Participants with eGFR 60-90 ml/min/1.73 m2, compared to eGFR >90 ml/min/1.73 m2, had a 2.90 mmol/mol (95% CI: 1.19, 4.61) lower HbA1c on sitagliptin than on canagliflozin (n = 342, P = 0.001). There were 2,201 adverse events reported, and 447/525 (85%) randomized participants experienced an adverse event on at least one of the study drugs. In this precision medicine trial in type 2 diabetes, our findings support the use of simple, routinely available clinical measures to identify the drug class most likely to deliver the greatest glycemic reduction for a given patient. (ClinicalTrials.gov registration: NCT02653209 ; ISRCTN registration: 12039221 .).
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Affiliation(s)
- Beverley M Shields
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - John M Dennis
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Catherine D Angwin
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Fiona Warren
- Clinical Trials Unit, University of Exeter Medical School, Exeter, UK
- Institute of Health Research, University of Exeter Medical School, Exeter, UK
| | - William E Henley
- Institute of Health Research, University of Exeter Medical School, Exeter, UK
| | - Andrew J Farmer
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Naveed Sattar
- School of Cardiovascular & Metabolic Health, University of Glasgow, Glasgow, UK
| | - Rury R Holman
- Diabetes Trials Unit, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Angus G Jones
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Ewan R Pearson
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Andrew T Hattersley
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK.
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Shields BM, Angwin CD, Shepherd MH, Britten N, Jones AG, Sattar N, Holman R, Pearson ER, Hattersley AT. Patient preference for second- and third-line therapies in type 2 diabetes: a prespecified secondary endpoint of the TriMaster study. Nat Med 2023; 29:384-391. [PMID: 36477734 PMCID: PMC7614215 DOI: 10.1038/s41591-022-02121-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 11/07/2022] [Indexed: 12/12/2022]
Abstract
Patient preference is very important for medication selection in chronic medical conditions, like type 2 diabetes, where there are many different drugs available. Patient preference balances potential efficacy with potential side effects. As both aspects of drug response can vary markedly between individuals, this decision could be informed by the patient personally experiencing the alternative medications, as occurs in a crossover trial. In the TriMaster (NCT02653209, ISRCTN12039221), randomized double-blind, three-way crossover trial patients received three different second- or third-line once-daily type 2 diabetes glucose-lowering drugs (pioglitazone 30 mg, sitagliptin 100 mg and canagliflozin 100 mg). As part of a prespecified secondary endpoint, we examined patients' drug preference after they had tried all three drugs. In total, 448 participants were treated with all three drugs which overall showed similar glycemic control (HbA1c on pioglitazone 59.5 sitagliptin 59.9, canagliflozin 60.5 mmol mol-1, P = 0.19). In total, 115 patients (25%) preferred pioglitazone, 158 patients (35%) sitagliptin and 175 patients (38%) canagliflozin. The drug preferred by individual patients was associated with a lower HbA1c (mean: 4.6; 95% CI: 3.9, 5.3) mmol mol-1 lower versus nonpreferred) and fewer side effects (mean: 0.50; 95% CI: 0.35, 0.64) fewer side effects versus nonpreferred). Allocating therapy based on the individually preferred drugs, rather than allocating all patients the overall most preferred drug (canagliflozin), would result in more patients achieving the lowest HbA1c for them (70% versus 30%) and the fewest side effects (67% versus 50%). When precision approaches do not predict a clear optimal therapy for an individual, allowing patients to try potential suitable medications before they choose long-term therapy could be a practical alternative to optimizing treatment for type 2 diabetes.
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Affiliation(s)
- Beverley M Shields
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Catherine D Angwin
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Maggie H Shepherd
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | - Nicky Britten
- Institute of Health Research, University of Exeter Medical School, Exeter, UK
| | - Angus G Jones
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Rury Holman
- Diabetes Trials Unit, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Ewan R Pearson
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Andrew T Hattersley
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK.
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DeMarsilis A, Reddy N, Boutari C, Filippaios A, Sternthal E, Katsiki N, Mantzoros C. Pharmacotherapy of type 2 diabetes: An update and future directions. Metabolism 2022; 137:155332. [PMID: 36240884 DOI: 10.1016/j.metabol.2022.155332] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/07/2022] [Accepted: 10/07/2022] [Indexed: 11/06/2022]
Abstract
Type 2 diabetes (T2D) is a widely prevalent disease with substantial economic and social impact for which multiple conventional and novel pharmacotherapies are currently available; however, the landscape of T2D treatment is constantly changing as new therapies emerge and the understanding of currently available agents deepens. This review aims to provide an updated summary of the pharmacotherapeutic approach to T2D. Each class of agents is presented by mechanism of action, details of administration, side effect profile, cost, and use in certain populations including heart failure, non-alcoholic fatty liver disease, obesity, chronic kidney disease, and older individuals. We also review targets of novel therapeutic T2D agent development. Finally, we outline an up-to-date treatment approach that starts with identification of an individualized goal for glycemic control then selection, initiation, and further intensification of a personalized therapeutic plan for T2D.
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Affiliation(s)
- Antea DeMarsilis
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA
| | - Niyoti Reddy
- Department of Medicine, School of Medicine, Boston University, Boston, USA
| | - Chrysoula Boutari
- Second Propedeutic Department of Internal Medicine, Hippocration Hospital, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Andreas Filippaios
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA
| | - Elliot Sternthal
- Section of Endocrinology, VA Boston Healthcare System, Harvard Medical School, Boston, MA 02115, USA
| | - Niki Katsiki
- Department of Nutritional Sciences and Dietetics, International Hellenic University, Sindos, Greece; School of Medicine, European University Cyprus, Nicosia, Cyprus.
| | - Christos Mantzoros
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA; Section of Endocrinology, VA Boston Healthcare System, Harvard Medical School, Boston, MA 02115, USA
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Pearson RJ, Grant C, Wijlaars L, Finch E, Bedston S, Broadhurst K, Gilbert R. Mental health service use among mothers involved in public family law proceedings: linked data cohort study in South London 2007-2019. Soc Psychiatry Psychiatr Epidemiol 2022; 57:2097-2108. [PMID: 35294976 PMCID: PMC9477900 DOI: 10.1007/s00127-022-02221-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 01/06/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE Mental health problems and substance misuse are common among the mothers of children who experience court-mandated placement into care in England, yet there is limited research characterising these health needs to inform evidence-based policy. In this descriptive study, we aimed to generate evidence about the type, severity, and timing of mental health and substance misuse needs among women involved in public family law proceedings concerning child placement into care ('care proceedings'). METHODS This is a retrospective, matched cohort study using linked family court and mental health service records for 2137 (66%) of the 3226 women involved in care proceedings between 2007 and 2019 in the South London and Maudsley NHS Mental Health Trust (SLaM) catchment area. We compared mental health service use and risk of dying with 17,096 female-matched controls who accessed SLaM between 2007 and 2019, aged 16-55 years, and were not involved in care proceedings. RESULTS Most women (79%) were known to SLaM before care proceedings began. Women had higher rates of schizophrenia spectrum disorders (19% vs 11% matched controls), personality disorders (21% vs 11%), and substance misuse (33% vs 12%). They were more likely to have a SLaM inpatient admission (27% vs 14%) or to be sectioned (19% vs 8%). Women had a 2.15 (95% CI 1.68-2.74) times greater hazard of dying, compared to matched controls, adjusted for age. CONCLUSION Women involved in care proceedings experience a particularly high burden of severe and complex mental health and substance misuse need. Women's increased risk of mortality following proceedings highlights that interventions responding to maternal mental health and substance misuse within family courts should offer continued, long-term support.
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Affiliation(s)
- Rachel J. Pearson
- Population, Policy and Practice Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, WC1N 1EH UK
| | - Claire Grant
- Department of Epidemiology and Public Health, UCL Institute of Epidemiology and Health, London, WC1E 7HB UK
| | - Linda Wijlaars
- Population, Policy and Practice Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, WC1N 1EH UK
| | - Emily Finch
- Southwark and Addictions Directorate, South London and Maudsley NHS Foundation Trust, London, SE5 8RZ UK
| | - Stuart Bedston
- Centre for Child and Family Justice Research, Lancaster University, Lancaster, LA1 4YW UK
| | - Karen Broadhurst
- Centre for Child and Family Justice Research, Lancaster University, Lancaster, LA1 4YW UK
| | - Ruth Gilbert
- Population, Policy and Practice Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, WC1N 1EH UK
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Franklin ZJ, Lafferty RA, Flatt PR, McShane LM, O'Harte FP, Irwin N. Metabolic effects of combined glucagon receptor antagonism and glucagon-like peptide-1 receptor agonism in high fat fed mice. Biochimie 2022; 199:60-67. [DOI: 10.1016/j.biochi.2022.04.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 04/05/2022] [Accepted: 04/13/2022] [Indexed: 01/19/2023]
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Brandon R, Jiang Y, Yeu RQ, Tweedie-Cullen R, Smallman K, Doherty G, Macaskill-Smith KA, Doran RJ, Clark P, Moffitt A, Merry T, Nehren N, King F, Hindmarsh JH, Leask MP, Merriman TR, Orr-Walker B, Shepherd PR, Paul R, Murphy R. Stratified glucose-lowering response to vildagliptin and pioglitazone by obesity and hypertriglyceridemia in a randomized crossover trial. Front Endocrinol (Lausanne) 2022; 13:1091421. [PMID: 36699039 PMCID: PMC9869378 DOI: 10.3389/fendo.2022.1091421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 12/02/2022] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Understanding which group of patients with type 2 diabetes will have the most glucose lowering response to certain medications (which target different aspects of glucose metabolism) is the first step in precision medicine. AIMS We hypothesized that people with type 2 diabetes who generally have high insulin resistance, such as people of Māori/Pacific ethnicity, and those with obesity and/or hypertriglyceridemia (OHTG), would have greater glucose-lowering by pioglitazone (an insulin sensitizer) versus vildagliptin (an insulin secretagogue). METHODS A randomised, open-label, two-period crossover trial was conducted in New Zealand. Adults with type 2 diabetes, HbA1c>58mmol/mol (>7.5%), received 16 weeks of either pioglitazone (30mg) or vildagliptin (50mg) daily, then switched to the other medication over for another 16 weeks of treatment. Differences in HbA1c were tested for interaction with ethnicity or OHTG, controlling for baseline HbA1c using linear mixed models. Secondary outcomes included weight, blood pressure, side-effects and diabetes treatment satisfaction. RESULTS 346 participants were randomised (55% Māori/Pacific) between February 2019 to March 2020. HbA1c after pioglitazone was lower than after vildagliptin (mean difference -4.9mmol/mol [0.5%]; 95% CI -6.3, -3.5; p<0.0001). Primary intention-to-treat analysis showed no significant interaction effect by Māori/Pacific vs other ethnicity (1.5mmol/mol [0.1%], 95% CI -0.8, 3.7), and per-protocol analysis (-1.2mmol/mol [0.1%], 95% CI -4.1, 1.7). An interaction effect (-4.7mmol/mol [0.5%], 95% CI -8.1, -1.4) was found by OHTG status. Both treatments generated similar treatment satisfaction scores, although there was greater weight gain and greater improvement in lipids and liver enzymes after pioglitazone than vildagliptin. CONCLUSIONS Comparative glucose-lowering by pioglitazone and vildagliptin is not different between Māori/Pacific people compared with other New Zealand ethnic groups. Presence of OHTG predicts greater glucose lowering by pioglitazone than vildagliptin. CLINICAL TRIAL REGISTRATION www.anzctr.org.au, identifier (ACTRN12618001907235).
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Affiliation(s)
- Rebecca Brandon
- Department of Medicine, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
| | - Yannan Jiang
- Department of Statistics, Faculty of Sciences, The University of Auckland, Auckland, New Zealand
- National Institute for Health Innovation, School of Population Health, The University of Auckland, Auckland, New Zealand
| | - Rui Qian Yeu
- Department of Medicine, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
| | - Ry Tweedie-Cullen
- Department of Medicine, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
| | | | | | | | | | - Penny Clark
- Ventures/Pinnacle Incorporated, Hamilton, New Zealand
| | - Allan Moffitt
- Procare Primary Health Organisation, Auckland, New Zealand
| | - Troy Merry
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
- Discipline of Nutrition, University of Auckland, Auckland, New Zealand
| | - Norma Nehren
- Te Hiku Hauora, Northland District Health Board, Kaitaia, New Zealand
| | | | | | - Megan Patricia Leask
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Tony R. Merriman
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, United States
| | | | - Peter R. Shepherd
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
- Department of Molecular Medicine and Pathology, School of Medical Sciences, The University of Auckland, Auckland, New Zealand
| | - Ryan Paul
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
- Department of Medicine, University of Waikato, Waikato, New Zealand
| | - Rinki Murphy
- Department of Medicine, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
- *Correspondence: Rinki Murphy,
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Boniolo F, Dorigatti E, Ohnmacht AJ, Saur D, Schubert B, Menden MP. Artificial intelligence in early drug discovery enabling precision medicine. Expert Opin Drug Discov 2021; 16:991-1007. [PMID: 34075855 DOI: 10.1080/17460441.2021.1918096] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Introduction: Precision medicine is the concept of treating diseases based on environmental factors, lifestyles, and molecular profiles of patients. This approach has been found to increase success rates of clinical trials and accelerate drug approvals. However, current precision medicine applications in early drug discovery use only a handful of molecular biomarkers to make decisions, whilst clinics gear up to capture the full molecular landscape of patients in the near future. This deep multi-omics characterization demands new analysis strategies to identify appropriate treatment regimens, which we envision will be pioneered by artificial intelligence.Areas covered: In this review, the authors discuss the current state of drug discovery in precision medicine and present our vision of how artificial intelligence will impact biomarker discovery and drug design.Expert opinion: Precision medicine is expected to revolutionize modern medicine; however, its traditional form is focusing on a few biomarkers, thus not equipped to leverage the full power of molecular landscapes. For learning how the development of drugs can be tailored to the heterogeneity of patients across their molecular profiles, artificial intelligence algorithms are the next frontier in precision medicine and will enable a fully personalized approach in drug design, and thus ultimately impacting clinical practice.
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Affiliation(s)
- Fabio Boniolo
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Munich, Germany.,School of Medicine, Chair of Translational Cancer Research and Institute for Experimental Cancer Therapy, Klinikum Rechts Der Isar, Technische Universität München, Munich, Germany
| | - Emilio Dorigatti
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Munich, Germany.,Statistical Learning and Data Science, Department of Statistics, Ludwig Maximilian Universität München, Munich, Germany
| | - Alexander J Ohnmacht
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Munich, Germany.,Department of Biology, Ludwig-Maximilians University Munich, Martinsried, Germany
| | - Dieter Saur
- School of Medicine, Chair of Translational Cancer Research and Institute for Experimental Cancer Therapy, Klinikum Rechts Der Isar, Technische Universität München, Munich, Germany
| | - Benjamin Schubert
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Munich, Germany.,Department of Mathematics, Technical University of Munich, Garching, Germany
| | - Michael P Menden
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Munich, Germany.,Department of Biology, Ludwig-Maximilians University Munich, Martinsried, Germany.,German Centre for Diabetes Research (DZD e.V.), Neuherberg, Germany
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