R&D spending in the pharmaceutical industry is peaking. While 2017 saw spending reach USD 165 billion, it is expected to keep climbing steadily over the coming years and is forecasted to hit USD 204 billion by 2024.
Although it is safe to assume that business is booming, it is more than likely that unless the cost of developing a new drug is contained, the industry will reach a tipping point where dwindling margins would discourage further investments and therefore advancement. In response, companies will have to shift gears by embracing emerging technologies such as advanced analytics, artificial intelligence (AI), machine learning (ML), and natural language processing, and become increasingly patient-centric.
While opportunities have been rife for AI adoption in life sciences and healthcare, the industry has been late in implementing these technologies. The intertwining sets of stakeholders have not helped the cause, with incompatibilities between administrators and doctors, and hospitals and patients. There has been a gap in knowledge and hesitation in using such technologies in a sensitive sector as life sciences and healthcare for a long time.
The enormous potential for AI/ML-driven technologies was largely untapped in the life sciences industry. This is quite ironical, considering how this particular industry thrives on cutting-edge technology and has required a boost in operational efficiency for a long time.
Clinical trials make for a compelling example. The processes that drive clinical trials make use of a large number of resources, including medical and scientific experts and doctors in every aspect. The costs to bring a drug to market continue to escalate, and highly qualified personnel have no other option but to engage in menial tasks such as quality control for regulatory submissions and producing first drafts of statistical analysis plans and protocols. Using AI/ML-driven automation, these processes and the overall clinical trial can be accelerated substantially.
Essentially, clinical trials are a crucial aspect of clinical research that currently requires huge manual labor. For instance, negotiating contracts between companies who have already negotiated before takes more time than it should, because it is impossible to furnish basic details such as previous contract terms correctly in quick time when it is humans who have to sift through a lot of paperwork. Having an AI-powered tool to go through several thousands of documents to bring out the relevant information can create a clear view of transactions between the companies to generate an appropriate contract.
A few other companies have already taken the leap forward by developing innovative drug delivery syste ms by incorporating technologies into their drug development plans. It has resulted in the birth of a new generation of drug-device hybrid products that are focused on improving patient experiences. The on-body injector is one such example – a small, lightweight delivery system designed to automatically administer a specific dose as a subcutaneous injection. While it allows doctors to remotely track and monitor patients, the device also ensures that users don’t have to visit a physician for undergoing a simple procedure.
It just goes to show that we have effectively stepped into the age of personalized medicine (PM). With a completely customizable group of therapies at its core, PM is focused on delivering patient-specific treatments designed to ensure safety and efficacy. A profile of a patient’s gene variations can guide the selection of drugs or treatment protocols that minimize harmful side effects and deliver better outcomes. PM can also be leveraged to predict an individual’s susceptibility to certain diseases before they manifest, allowing physicians and patients to design a plan for monitoring and prevention. Physicians can now go beyond the conventional, one‑size-fits-all model for prescribing drugs and make data-driven clinical decisions for each patient. Pharmacogenomic testing is being used commercially for some commonly prescribed drugs such as tamoxifen for cancer and warfarin to manage blood coagulation. These techniques require smaller trials and advanced computing and technology aids.
Interestingly, as these advances are possible, the regulators are also beginning to think about taking advantage of these trends. They have the onerous responsibility of ensuring not only the statistical significance but also the clinical efficacy of the treatment along with cost effectiveness. The United States Food and Drug Administration (US FDA) is working toward the use of artificial intelligence and other digital tools in medicine and drug development. They are establishing a new incubator focused on health technology. In May 2018, the US FDA permitted marketing of Imagen OsteoDetect. The OsteoDetect software is a computer-aided detection and diagnostic software that uses an artificial intelligence algorithm to analyze two-dimensional X‑ray images for signs of distal radius fracture, a common type of wrist fracture by marking the location of the fracture to aid in detection and diagnosis. The FDA reviewed the OsteoDetect device through the De Novo premarket review pathway, a regulatory pathway for some low‑to‑moderate risk devices of a new type. OsteoDetect is intended to be used by clinicians in various settings, including primary care, emergency medicine, urgent care, and specialty care, such as orthopedics.
The pharmaceutical industry is looking to find effective treatments for serious conditions that fill an unmet need by accelerating the drug development process and seeking approval from regulators along with leveraging real-world data and evidence for finding solutions. The regulators have begun making appropriate regulatory changes to take into account real‑world evidence‑based validation and also approval for newer treatments based on evaluation of benefits vs risks in some circumstances. The FDA has regulations that allow for Accelerated Approval/Fast Track Approval processes for drugs that treat serious conditions or that fill an unmet medical need (orphan diseases, eg, cystic fibrosis, Tourette’s syndrome, Hamburger disease). The approval is based on surrogate endpoints or measurements that predicts how well the drug may work.
Business 4.0TM principles are likely to enable abundance of resources, provided the pharmaceutical companies and regulators are able to get beyond the challenges of maintaining secrecy of intellectual property. Facing these circumstances by changing the mindset is the key for progress. This can be done by adopting agile methods, collaborating with the regulators and patients, and by taking advantage of collective industry‑wide effort to create effective treatments at affordable costs. Service bundling seems to be the future outsourcing strategy. This will drive efficiency with service providers by helping companies have an end-to-end process view that will override traditional outsourcing boundaries. This can lead to reduced costs and increased flexibility helping pharmaceutical companies to simplify multiple functions. Service bundling can happen across trial planning, conduct, technology, and related reporting services in a single platform.
As we look forward to the next 5 years, there emerges great opportunities to deepen and broaden adoption of technology by developing integrated solutions. There is a significant potential to cocreate solutions with pharmaceutical companies and the regulators to serve patients’ needs