Early scientific research into the complexity and clinical or mechanistic heterogeneity of common chronic diseases, however, has started to break down this seemingly insurmountable barrier. In heart failure, for example, we are not looking at the heart in isolation but at patients as a whole, many with multiple co-morbidities such as obesity and diabetes which may influence the progression of heart failure; adding to the disease complexity and affecting the likely response to heart failure therapies. In COPD, an estimated 500 different genes are expressed abnormally in a sample of diseased lung tissue, with considerable heterogeneity in expression of these genes between patients.1 Many of these could play a mechanistic role and represent a viable drug target. Indeed, we can think of COPD as multiple diseases, more like a syndrome than a single disease, and with all the complexity that entails. Many chronic diseases may be thought of as syndromes of multiple diseases, akin to the revolution in the diagnosis of non-small cell lung cancer, where previously the disease was defined based on histological subtypes but now the use of detailed molecular profiling aids diagnosis and treatment selection.2
Currently the management of many complex chronic diseases is the same for most patients, based on clinical diagnosis and symptomatic treatment. This limits opportunities to slow or halt disease progression leading to potentially sub‑optimal outcomes for some patients. However, with evolving research, this paradigm is changing.
Uncovering the drivers of chronic diseases
Digging deep into the biology of chronic diseases to uncover genetic and molecular drivers has guided the identification of new biomarkers and the development of linked precision diagnostic tests for novel, targeted therapies.
At AstraZeneca, we are working to identify genetic disease drivers and biomarkers to reflect the many comorbidities and redefine patient phenotypes. Using multi-omic technologies, such as genomics, proteomics and metabolomics, we can identify the most appropriate genetic, protein or other targets for different patient groups according to their underlying disease mechanism. With this understanding, we can compare the biological pathways of these sub-populations to see which is disproportionally involved in causing disease and potentially enable earlier and more accurate diagnosis.
Across respiratory and immunological diseases, for example, we have identified biomarker‑led sub‑groups of patients dependent on pro‑inflammatory cytokines such as interleukin 33 (IL-33), a broad acting, damage-responsive cytokine, with distinct biology in asthma including airway hyperreactivity immune response, sub epithelial fibrosis and mucus hypersecretion.3,4 In a disease like heart failure, we are working to understand the whole molecular landscape. Gene expression profiling aims to identify genes expressed differently in a diseased heart compared with a normal heart, in the presence or absence of comorbidities. All the proteins and lipids in a diseased heart can be measured and compared to those in a healthy one, as well as the molecules that are intimately involved in metabolic processes.
To understand these hugely complicated datasets arising from multi-omic analyses, the latest artificial intelligence (AI) and machine learning (ML) technologies are used to discover associations between data and diseases including the development of knowledge graphs describing the molecular interconnections and interdependences within a disease. Without these advances, it is almost impossible to imagine being able to make sense of all the exciting data we are accumulating.
Identifying patients likely to benefit
Our ambition is to develop diagnostic-led therapeutics to halt progression of these often degenerative, debilitating and life-threatening conditions, achieve remission and one day cure them”
Clinicians currently rely on clinical symptoms and limited disease diagnostics to sub-divide chronic disease patient groups – and these can overlap. To move precision medicines for these diseases into clinical practice, it is essential to identify tools for accurate, robust diagnostics of response alongside the drug.
Biomarkers must accurately identify patients under a range of physiological conditions, with minimal false positives or negatives, and be easily used in routine clinical practice with marginal inconvenience. A blood or urine test is likely to be the preferred option, but this does not always represent the molecular landscape within the target organ. In asthma and COPD, nasal sampling is being explored as a relatively non‑invasive alternative to sampling directly from the lung. Another consideration is the technology needed to analyse samples, as this must be widely available in the clinical setting. For example, if levels of a biomarker are so low that mass spectrometry is needed for measurements, this may not be practical outside a research centre. Equally, any natural variations of the biomarker, for example due to patient activity rather than disease, must be fully understood before it can be deployed in the clinic.
A new era of drug discovery and diagnostics
At AstraZeneca, we are committed to moving beyond current standards of care for chronic diseases, where patients progressing along the disease pathway are treated in the same sequential way. Our ambition is to develop diagnostic-led therapeutics to halt progression of these often degenerative, debilitating and life‑threatening conditions, achieve remission and one day cure them.
Our 5R research framework (right target, right patient, right tissue, right safety, right commercial potential) has helped progress our maturing precision medicine pipeline in chronic diseases in the past decade, by integrating state-of-the-art technologies to identify new targets and the latest translational science into human studies.5,6
As part of this progression, we recognise the specialist expertise required for the development of diagnostic-led precision medicines. Having collaborated with leading diagnostics partners in the development of our precision medicines in oncology, we are now establishing similar partnerships in complex chronic diseases.
Precision medicine in practice
Oncologists are familiar with the value of performing molecular profiling on tumour samples to guide treatment decisions. However, the potential of precision medicine to increase the probability that patients will get the right treatment first time will be new to many clinicians caring for patients with complex chronic diseases. In addition to providing education at all levels about the future role of precision medicine, innovative financing and regulatory mechanisms will be needed to support the uptake of precision medicines in chronic diseases. Standardised, well validated tests will also need to be readily available and cost effective, with results easily interpretable to guide treatment decisions in daily practice.
Key to the success of this revolution in patient care will be powerful data and clear evidence that precision medicine is worthwhile to patients, clinicians and payers. At AstraZeneca, we are committed to working collaboratively with patients and healthcare systems to realise this potential and improve lives.
About the authors
Dr Maria Orr is Head of Precision Medicine for BioPharmaceuticals in AstraZeneca. In this role she leads the delivery of precision medicines and their associated companion diagnostics for drugs across a diverse range of therapeutic areas including cardiovascular, renal, metabolism (CVRM), respiratory and immunology (R&I), microbial science and neuroscience. Maria has extensive experience in the field of precision medicine and companion diagnostic development and has contributed to the successful launch of personalised treatments and companion diagnostic assays in oncology.
Dr Adam Platt is Vice President Translational Science and Experimental Medicine for Respiratory and Immunology BioPharmaceuticals R&D in AstraZeneca, with teams across the UK, Sweden and the US, bringing new targets and biomarkers from novels sources and delivering a precision medicine approach to the pipeline. Previously, as Global Head of Genomics Portfolio in AstraZeneca’s Centre for Genomics Research, Adam was critical in setting up the initiative to integrate genome sequence and clinical data from up to two million patients to transform AstraZeneca’s drug discovery.
Dr Ben Challis received both his PhD and MD from the University of Cambridge and is currently the Vice President and Head of Translational Science and Experimental Medicine in Early Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D at AstraZeneca, where a precision medicine research approach is applied from the start to address an individual’s disease depending upon the biomarkers they expressed. Ben also remains an Honorary Consultant in Endocrinology at Addenbrooke’s Hospital in Cambridge, UK.
References
- AstraZeneca data on file
- Hirsch FR, Suda K, Wiens J, et al. New and emerging targeted treatments in advanced non-small-cell lung cancer. Lancet. 2016;388:1012–24
- Kaur D, Gomez E, Doe C, et al. IL-33 drives airway hyper-responsiveness through IL-13-mediated mast cell: airway smooth muscle crosstalk. Allergy. 2015;70(5):556-67
- Allinne J, Scott G, Lim WK, et al. IL-33 blockade affects mediators of persistence and exacerbation in a model of chronic airway inflammation. J Allergy Clin Immunol 2019;144(6):1624-37.
- Morgan P, Brown D, Lennard S, et al. Impact of a five-dimensional framework on R&D productivity at AstraZeneca. Nat Rev Drug Discov 17, 167–181 (2018).
- Dugger S, Platt A, Goldstein D. (2018) Drug development in the era of precision medicine. Nature Reviews Drug Discovery. 17 (3):183-196. doi:10.1038/nrd.2017.226 Published online 8 Dec 2017
