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Focusing on prevention, diagnosis, treatment, and care monitoring during the patient journey in oncology to create value for stakeholders.
Life cycle management is often viewed as a discrete event in product development and commercialization. However, it must be considered a continuous process that allows biopharma companies to increase a product's value over its lifetime. This article outlines the importance of the patient-centric approach in uncovering life cycle opportunities in the oncology therapeutic area as the field is rapidly evolving. This patient-centric approach broadly focuses on all aspects of the patient journey (prevention, diagnosis, treatment, and care monitoring) and outcomes that create value for stakeholders.
Despite enormous advances in science and therapy, significant unmet needs persist in many cancers. Mortality and morbidity rates have generally been improving across a range of cancers. For example, improved screening and awareness efforts, coupled with new treatments, have led to significant improvements in breast cancer survival rates. Some cancers have been turned into chronic diseases (e.g., CML), and cures seem to be possible (e.g., melanoma) if detected earlier. The number of deaths attributed to breast cancer has diminished by almost 20% over the past decade.
Personalized and targeted therapies are reshaping the treatment paradigms. Targeted therapies ranging from immunotherapies to next-generation biotherapeutics (cell therapies, gene therapies, nucleotide therapies, cancer vaccines) are becoming commonplace. Per GlobalData, "we are getting to about 30% of the patients on immunotherapies being cured. So, we still have the burden of the other 60–70% where the treatments are sub-optimal, and ultimately many of these patients tend to progress."
In 2018, larotrectinib became the second tissue-agnostic oncology therapy to be approved, following the pioneering approval of pembrolizumab in 2017. These drugs signal a paradigm shift towards the treatment of tumors based on genetic profile rather than site-of-origin in the body, addressing specific issues regarding how solid tumors were treated prior to such developments. The development and use of antibody-drug conjugates (ADCs), and bispecific antibodies have become a key growth area–traditional immunoglobulin antibodies having been further engineered to improve safety and efficacy. The NIH finds that with ADCs, the cancer-selective delivery of potent cytotoxic payloads may eradicate target-expressing cancer cells while sparing normal healthy tissue.
Next-Generation Sequencing (NGS) is being used more frequently to aid diagnostics and determine appropriate therapeutic interventions for patients. An NGS-based comprehensive gene panel test allows providers to identify gene alterations, which can then be targeted by molecular drugs. The new paradigm might be to make targeted therapy a first-line option. For example, an NGS test from the tissue or a liquid biopsy is ideal for identifying ALK+ NSCLC patients who could benefit from one of the many approved targeted TKI therapies.
One of the critical challenges in oncology is that the clinical trial success rates are still low. To increase clinical success rates, biopharma companies are focusing more on better patient selection and adaptive trials. Despite clinical/regulatory success, the market access for many of these approved diagnostics, drugs, and devices is not guaranteed. The providers, payers, policymakers, and advocacy groups are demanding that biopharma companies show value in terms of the benefits and cost-effectiveness of their products.
Understanding the patient journey is essential to uncover unmet needs and to devise the best medical and non-medical interventions. There is no doubt that our understanding of these cancers is increasing at a rapid pace, and new technologies are allowing us to diagnose and treat these cancers better. In this section, we present various approaches used to manage each stage of the patient journey (diagnosis, treatment, and care management) from a life cycle management perspective.
Biomarkers play an essential role in properly diagnosing cancers—they exist in some cancers and do not exist in many others. Some tend to be nuanced; for example, in widely adopted immuno-oncology drugs, PD-L1 tends to be a weak biomarker for predicting response to immunotherapy. Many cancer drugs have been approved in recent times with companion diagnostics, which are facilitating personalized medicine approaches. One of the early successes was Zelboraf approved for people whose inoperable or metastatic melanoma carries a BRAF V600E mutation, which can be determined by cobas BRAF mutation test. Keytruda’s pan-tumor FDA approval in May 2017 was the first case where a product was approved based on biomarker expression rather than tumor location.
Today, liquid biopsies are used to detect traces of cancer DNA in the blood for proper diagnoses (early detection of cancer or to find out how well cancer treatment is managed, or whether it has relapsed). They are also used in cell/gene therapies as blood samples over time can indicate the molecular changes in a tumor. As Foundation Medicine suggested in 2020 that liquid biopsies are particularly helpful in some cancers (e.g., NSCLC), where a significant portion of patients are found not to have adequate tissue available for diagnosis using standard biomarker tests. Molecular testing and genomic testing are common approaches used to understand resistance mutations better.
Artificial Intelligence (AI) and Machine Learning (ML) tools are complementing areas in which valid biomarkers don't exist or have limitations. An early AI system was developed by a consortium led by the German Cancer Research Center in Heidelberg–the team used a methylation method to sort medulloblastomas into sub-types, covering approximately 100 known cancers of the CNS (published in Nature, 2018). Recently, the AI Classifier developed by NYU Langone’s Perlmutter Cancer Center was approved for use as a diagnostic test in Oct 2019–the test predicts which patients with certain types of skin cancer (in particular, metastatic melanoma) would respond well to immunotherapy. The goal here is to leverage technology to stratify cancers better to develop the targeted and personalized approaches to treat them.
Treatment optimization has taken on added significance in the age of precision medicine in oncology. Though monotherapies are generally preferred, combination therapies are increasingly becoming the mainstay to treat difficult cancers.
Predicting likely treatment response is key as many cancer patients tend to relapse or disease progresses. For example, the Tumor Mutational Burden (TMB), a measure of the number of gene mutations within cancer cells, is emerging as a useful predictive biomarker for checkpoint inhibitors. Cells with high TMB are more likely to be abnormal and attacked by the immune system. Predicting disease progression is allowing providers and patients to help predict relapses. For example, Oncotype DX is a 21-gene breast cancer test that predicts the 10-year likelihood of breast cancer recurrence for patients treated with tamoxifen, as reported by Orucevic et al of the University of Tennessee Medical Center. Cancer is now commonly understood as a disease of pathways rather than defects in individual genes. Therefore, network-based approaches are being used to assess how mutations differ in similar disease phenotypes. Gene interaction networks identify defective pathways, classify subtypes based on subnetworks, and predict treatment and survival outcomes. In addition to gene networks, patient similarity networks are gaining importance and can offer different perspectives for understanding cancer.
Cancer patients must be closely monitored during the treatment period while accounting for physical, emotional, financial, and social burdens. The treatment effectiveness is commonly assessed using patient-reported outcomes (PROs) such as disease symptoms, physical function, and symptomatic adverse reactions. Close monitoring allows biopharma companies to uncover issues that could be addressed using devices and technologies beyond medical interventions.
Biopharma companies are still in the early stages of harnessing the power of PROs during clinical development and in a real-world setting. Significant challenges exist in terms of how PROs can be applied across various cancers. The health-related quality-of-life data is often challenging to customize at the individual patient level, especially on the social, emotional, and cognitive levels. A 2019 review by the FDA indicated that more clarity and consistency are needed to report PROs and HRQoL data. Most oncology treatments present tolerability challenges, and capturing PROs is vital to assess the drug's overall effectiveness in the real-world setting. Several clinically validated instruments are being introduced to measure PROs, such as:
Though one might expect high compliance in cancer patients given the seriousness of the disease, the compliance rates tend to be suboptimal due to various issues ranging from treatment effectiveness to patient motivation and socio-economic challenges. Patient engagement requires monitoring tools in the real-world setting. Novartis partnered with IBM Watson to develop a cognitive solution that uses RWE to predict breast cancer treatment response. Numerous smartphone apps are now available to help cancer patients monitor their disease progression and adherence (examples include BELONG, Cancer.net Mobile, and Carezone). Providers and support groups focus on non-pharmacological interventions such as patient education, exercise, stress management, cognitive therapies, nutrition, and hydration.
In "Crossing the Quality Chasm," the Institute of Medicine defines the six aims of quality healthcare – the care should be safe, effective, patient-centered, timely, efficient, and equitable. In the context of patient care and ongoing monitoring, biopharma companies must explore ways to positively impact overall patient outcomes by focusing on treatment optimization and patient monitoring in the real-world setting.
In the early stages of drug development, biopharma companies take a conservative approach to life cycle investments until proof of concept is established. Once proof of concept is established, these companies must aggressively pursue life cycle opportunities to maximize the asset's lifetime value. We suggest biopharma companies take a patient-centric approach to uncover life cycle opportunities for long-term impact and success. The companies could leverage the below framework to uncover a wide range of opportunities to develop evidence-based and value-based solutions to support patients better. Once these life cycle opportunities are identified, their value and impact could be assessed along with the development/regulatory feasibility before embarking on a development journey. Ultimately, these life cycle opportunities must create value to patients and other stakeholders to ensure adoption and market access.
Subbarao Jayanthi, Managing Partner of RxC International, Nick DeSanctis, Executive Partner at RxC International