Precision medicine (PM) strategies are driving an era of value-based care to make diagnosis, treatment, and disease management more targeted and patient-centric, improving treatment outcomes and reducing overall healthcare costs. PM’s utility is expanding across the continuum of healthcare to achieve equitable, outcome-driven healthcare delivery. The PM will find high clinical utility for diseases where over and undertreatment could lead to resistance and serious side effects.
Oncology is the most tapped area, where PM tools such as whole genome sequencing (WGS) and whole exome sequencing (WES), and targeted gene panels are being increasingly used to predict disease risk, stratify patients for therapy selection, and monitor response to therapy. It can also be useful to predict disease recurrence and optimize dosage. Precision tools are expected to improve the efficiency and accuracy of disease screening, risk assessment, prevention, diagnosis, to therapy selection, and monitoring while reducing costs and improving treatment outcomes.
Advanced Precision Testing Aimed to Provide Holistic Molecular Signatures
While genetic, environment, and lifestyle factors were initially the key factors considered to develop precision medicine tools, advances in multiple omics technologies, single cell analysis methods, molecular imaging, big data, and artificial intelligence (AI) augmentation are pushing the realm of PM by providing deep molecular insights. Precision testing has expanded beyond just genomic analysis and includes multiple biomarkers–microbiome, transcriptome, epigenome, and proteome that provide a detailed molecular signature which would be a value add in disease modeling, drug development, clinical decision support making, and enabling real-time, dynamic monitoring of the disease. Pharmacogenomics assays can also guide and predict the response in patients and help optimize treatment. Targeted gene panels are being increasingly utilized to assess cancer risk, while companion diagnostics have been developed for the rational use of targeted therapies in breast, lung, and other cancers.
Single-cell analysis technologies, especially single-cell multi-omics tools, and spatial omics are emerging as promising tools for PM. These methods give a deep understanding of tumor heterogeneity and can be leveraged in precision oncology. While PM aims to help stratify patients and decide on treatment regimens, it can be a useful tool for disease monitoring and surveillance and enable a shift towards predictive and preventive healthcare in the long term. In an era dominated by omics and Big Data, advanced analytical tools which can integrate multidimensional, heterogenous, large-scale data could decipher patterns from “big” complex data and hold great potential to advance PM.
Targeted Precision Therapies
High costs and poor treatment outcomes, resulting in tumor resistance have been key in the development of targeted cancer therapies. The US Food and Drug Administration (FDA) approved targeted therapies, Herceptin and Gleevec which have been in the market for several years are a testament to the groundwork done in this space. Pharma companies are increasingly moving away from the blockbuster mindset to create more high-value, targeted therapies for target certain patient populations. Implementation of molecular screening and subsequent therapy selection is becoming more commonplace for certain types of cancers, with an array of FDA-approved targeted therapies, and a strong pipeline of late-stage candidates. The accelerated approvals of mobocertinib and sotorasib in 2021 as targeted therapies for different mutations in NSCLC indicate the growth of genomic tools-driven therapeutics development. Several Companion Diagnostics (CDx) is being co-developed with therapeutics, and these assays are being widely leveraged to predict treatment outcome, help stratify patients for optimal drug and dosage, and for prognosis.
Augmenting Clinical Testing with Digital Tools and AI is Integral to Realizing the Full Potential of PM
The growing demand for precision medicine has certainly pushed digital transformation and has made digitization from a good-to-have to a critical element in healthcare. Big Data and AI/machine learning (ML) tools help in managing high volume, disparate multimodal data from clinical data, and electronic health records (EHRs) along with omics data and enable precision diagnostics, and support as CDS (clinical decision support) tools.
AI/ML augmentation can enhance the accuracy of molecular diagnostics and enable the prediction of disease risk and early onset. This is especially important for complex diseases, such as cancers or neurological diseases, where a panel of biomarkers needs to be analyzed. However, the limited clinical validation of such tests and reimbursement challenges have been deterrents to widescale adoption.
Many AI-based molecular diagnostics are being developed as an adjunct to treatment intervention, to analyze responses to treatment, and predict outcomes. In addition to cancers, the use of molecular signatures is critical in infectious diseases where overtreatment can result in antimicrobial resistance (AMR), a globally growing concern. Advances in diagnostics integrated with digital tools have made infectious disease management more precise than ever. Rapid phenotypic or culture-free antibiotic susceptibility testing platforms can quickly detect and identify infectious diseases within minutes, and this also enables precise treatment of the infection, rather than treating patients with broad-spectrum antibiotics. Rapid immunodiagnostics and host response tests can be used to differentiate between bacterial and viral infections and support as a CDS tool. Harmonizing the integration of AI into genomic diagnostics will support the prediction of infectious pandemics, better understand the prognosis of infections, and provide actionable insights to clinicians to initiate appropriate therapeutic intervention.
In critical care or emergency care, advanced digital tools are extremely valuable for precise clinical decision-making and improved outcomes. The AI/ML algorithms can analyze bedside tests, EMRs, medical imaging, and Web- and mobile-based device data for precise identification of patients’ needs and provide appropriate treatment. The AI-enabled clinical decision-making platform can also detect clinical risks in patients to facilitate early individualized interventions.
Wearable devices such as smartwatches, or skin patch-based diagnostics are integrated with flexible and miniaturized sensors measuring simple parameters such as vital signs, stress, wellness, or glucose monitoring to infection, sleep, or ECG monitoring. The continuous health data collected by digital tools can be analyzed to identify the patterns in a patient’s health, which are called digital biomarkers. Integration of AI algorithms with digital biomarkers can provide actionable diagnosis and precise selection of therapy or dosage to the patient at the right time. This is especially useful for providing precision medicine for chronic disease patients. For example, healthcare professionals performing digital biomarker analysis of patients with diabetes, and hypertension can receive real-time insights such as dynamic dose optimization or precision nutritional recommendations. Digital biomarkers can also be useful to pick early “signals” to predict and prevent certain diseases and quantify phenotypic data. In fact, pharma companies are providing users with digital tools such as wearables, companion apps, or AI health assistants to get personalized insights and manage their disease condition by optimizing dosage by themselves.
Medical imaging biomarkers can help in stratifying the patients and depending on the location and extent of the abnormality, the treatment decisions are made accordingly. Hybrid imaging can also be used to guide precision medicine in patients. Such as fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) imaging modality can guide precision medicine for invasive bladder carcinoma. Moreover, combining data from medical images with individual genomic phenotypes—also known as radio-genomic analysis—helps in guiding therapeutic strategies for individuals. This diagnostic, therapy, and prognostic precision medicine plays an important role in oncology.
Advanced Clinical Trials for Precision Drug Development
With the aim to develop precision medicine, the clinical trial design is also changing significantly. Adaptive trial design analyzes interim data points to modify the trials as required and provide precise therapy for patients. These trials modify the doses and better assess the relationship between outcomes and dosage. Digital biomarkers also support precision medicine in clinical trials by stratifying the population into different subsets to strategic treatment planning. A digital biomarker is an effective way of developing personalized population baseline data which improves the pre-screening and recruitment of patients in a trial.
Current Roadblocks and the Path Ahead
With dwindling costs and times of next-gen sequencing, WGS has become routine in clinical practice. However, there is still a significant underrepresentation of certain populations and ethnicities in terms of SNP (single nucleotide polymorphism) mapping, and biobanking, and this needs to change to derive value from precision testing. Issues around regulations, insurance reimbursements, and data management and analysis have also impeded the full-fledged development of PM. Most genetic predictive testing is not reimbursed in many parts of the globe, and improving clinical validation and including their coverage would help in widespread adoption. Developing targeted medicines is more R&D intensive and costly than traditional medicines and requires streamlined regulations for their success. Outcome-based reimbursements can help such high-value, low-volume therapies thrive in the market and make them more accessible. Increased use of deep learning and other ML algorithms can help analyze large-scale, multimodal data to make PM more streamlined. For the PM to become an integral part of disease management, it is important to devise strategies to integrate genomic data, clinical data, and RWE (real-world evidence) in real time to render precision diagnostics and population health analysis more clinically actionable.
Large Scale Precision Medicine Efforts Will Bolster Growth
The Precision Medicine Initiative launched by the US in 2015 was the first formal national-level effort to implement strategies to shift towards value-based care delivery. This was followed by nations such as UK, Sweden, and Germany setting up PM initiatives. Multicentric efforts such as the one between Genomic Medicine Sweden (GMS) and the Centers for Personalized Medicine (ZPM) initiative in Germany are collaborating to advance PM. Countries in the GCC (Gulf Cooperation Council) region, especially UAE, Saudi Arabia, and Qatar are also actively working on large-scale genome sequencing, biobanking, and creation of reference genomes (Qatar Reference Genome) which would drive population-wide understanding of disease susceptibility and help develop preventive healthcare strategies. China and Japan are trailblazers in the APAC region, while Taiwan and others have also prioritized large-scale genomic sequencing efforts to push PM. It recently announced precision health as one of its six core strategic focus areas in its Vision for 2030. Building comprehensive datasets from diverse populations, and inclusion of underrepresented minorities will be critical to further drive PM strategies. Developing sustainable and long-term economic models that would support the financing of PM strategies will be crucial for widespread adoption.
Ruplekha Choudhurie, Senior Industry Analyst and Team Lead (Health & Wellness), TechVision, Frost & Sullivan, Debarati Sengupta, Senior Industry Analyst and Team Lead (Medical Device and Imaging), TechVision, Frost & Sullivan
Blog received on Mail by Frost & Sullivan
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