Uncover Potential Life Cycle Opportunities in Oncology Using Patient-Centric Outcomes

March 19, 2021
Subbarao Jayanthi

Nick DeSanctis

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.

Key developments shaping the oncology market

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.

Opportunities using a patient-centric approach

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.

Diagnosing the disease

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

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.

  • Personalized treatments: Treatment paradigms are continually shifting from early conservative treatments to aggressive treatments to contain growth from early on. With clear evidence that each patient responds to treatments differently given the genetic predispositions, physicians must personalize treatments across therapy lines. When considering earlier and later therapy lines, sometimes it's a common practice for new drugs to start with later lines to see how the drug works. If the drug shows significant efficacy and has a compelling safety/tolerability profile, it is studied for use in earlier lines of therapy. For example, Tasigna (nilotinib) was initially approved for chronic-phase and accelerated-phase Ph+ CML in adult patients resistant or intolerant to prior treatment, including Glivec (imatinib). Within a few years, the product was later approved for use in newly diagnosed CML patients as the company produced clinical evidence.
  • Combination treatments: If monotherapies are suboptimal, it’s logical to pursue combination opportunities as alternative pathways assuming synergic effect or dual effect. Most cancer patients tend to develop drug resistance, which could only be addressed using other drugs or combinations. Genotype predictions may be vital in finding the best mono or combination treatments for these types of patients. Checkpoint modulators, bispecific antibodies, and oncolytic viruses are increasingly being developed as combination regimens. For example, the checkpoint modulators have thousands of ongoing investigational combination studies with other IO/non-IO agents.
  • Patient characteristics and subpopulations: One of the key challenges with most oncology drugs is managing the side effects (e.g., fatigue, neutropenia, GI toxicity). Clinical studies provide early insight into what the potential adverse events (AE) are, but the complexity of managing them increases significantly in a real-world setting. Understanding patient characteristics (e.g., older frail patients vs. younger healthy patients) presents significant opportunities if patients are stratified better using appropriate data (e.g., genomic and proteomic data) and tools. Less aggressive treatments are generally preferred for older patients as they tend to have lower resistance, higher side effects, and a higher prevalence of co-morbidities coupled with a fragile immune system. Using safer and tolerable drugs to maintain the quality of life without allowing cancer to spread may often be the goal while treating these patients.
  • Dose optimization: Early dose-ranging studies provide useful insights into what the optimal dose might be; this dose is commonly studied in clinical trials. However, dose optimization to account for patient preferences and characteristics continues to be a challenge in a real-world setting. Given the development pressures, biopharma companies often advance programs without a clear understanding of the dose-response relationship, which is critical even after approval to further enhance the drug's benefit-risk profile.
  • Product delivery: Drugs must be efficiently delivered to the intended targets while minimizing the off-target effects. Many new technologies facilitate better delivery options for drugs, whether in the development stage or already on the market. The use of nanoparticles in drugs are used to target tumors directly, and they are being used in gene therapies as well. For example, in glioblastoma patients, the intratumoral delivery of drugs that target tumor(s) is being evaluated using nanoparticle technologies. While drug targeting has primarily focused on tissue components and cell vicinities in the past, it is the membranous and subcellular trafficking system that directs the molecules to plausible locations. Specialized methods promoting the subcellular targeting with minimal off-target effects do exist. Another technology that received immense attention is Small interfering RNA (siRNA) for increased target specificity.

Treatment monitoring

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.

Patient care management

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:

  • The FDA’s Office of Hematology and Oncology Products (OHOP) has identified symptomatic adverse events (AEs) as a central PRO concept. A systematic assessment of patient-reported symptomatic AEs can provide data to complement clinician reporting. EORTC-QLQ instruments are also widely adopted in research and practice settings across many cancers.
  • The NCI’s Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) measures symptomatic AEs from the patient perspective. PRO-CTCAE is a standardized measurement system that can provide a flexible fit-for-purpose approach to assess relevant symptomatic AEs across a broad range of cancer therapies.
  • Digital health technologies enable remote electronic PRO assessments as a clinical decision support tool to facilitate meaningful provider interactions and personalized treatment. For example, the Strength Through Insight app uses Apple ResearchKit software with content from the validated HRQoL tools 26-item Expanded Prostate Cancer Index Composite for clinical practice 8-item Functional Assessment of Cancer Therapy Advanced Prostate Symptom Index.

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.

How to uncover LCM opportunities in oncology

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