In the dynamic world of pharma, achieving efficacy and precision – two seemingly simple goals – is still a constant pursuit. The drug development lifecycle is long and complex, requiring innovation to achieve breakthroughs. Efficacy is the lynchpin in this process, and precision, a spin-off from precision medicine, promises tailored treatments and better outcomes.
In this context, AI in pharma is a transformative force. Its impact resonates across the entire value chain, from enhancing comprehension of metabolic pathways and receptors in the discovery phase to designing superior molecules and biologics. The industry expects AI to optimize product launches, improve patient interactions, and enhance manufacturing efficiency – all of which should translate into higher returns on invested capital.
Despite these promises, why the hesitation in fully embracing AI? Larger companies grapple with identifying high-value use cases and seamless integration. For startups, those challenges double as they must demonstrate tangible proof that AI investments yield substantial returns.
So, let’s explore how AI tackles industry challenges, discuss impactful business cases, and learn how to ensure the success of AI-related investments.
Issues in Clinical Development and How AI Can Address Them
It would be a tough call if you asked me to pinpoint the most resource-intensive domain in the pharma industry. Yet, I’d place my bet on clinical development. It demands heavy investment in research and trials until the critical inflection point. This critical phase validates the product’s effectiveness and safety through rigorous trials, potentially unlocking opportunities for commercialization or partnerships with larger pharmaceutical firms.
Understanding how AI optimizes resource allocation during clinical development is essential. But, frankly, there’s scarcely a domain – be it drug manufacturing, life sciences, or the broader pharmaceutical landscape – where AI doesn’t find application and value.
Design of Clinical Trials
Issues: Clinical trial designs and patient profiling are fundamental for successful trials. Yet, the complexity of trial designs and the irreversible consequences of post-protocol approval demand meticulous planning.
AI’s impact: By leveraging predictive analytics and patient data profiling, AI enhances the design process. It articulates inclusion criteria and facilitates simulations and refinements, ensuring a strategic and precise protocol. This approach expedites patient enrollment, streamlines the trial’s inclusion criteria, and employs advanced machine-learning algorithms to analyze diverse datasets. This speeds up a trial timeline and makes trial execution more precise.
Failure to Enroll the Right Responders
Issues: The success of a clinical trial hinges on obtaining reliable outcomes, making the enrollment of the right participants a critical factor. While it may seem straightforward, it’s actually a challenge. Strict criteria for participant inclusion and exclusion add complexity, making it difficult to find individuals who meet specific requirements. The diverse nature of patient populations, with varying medical histories, demographics, and genetics, further complicates the process. Ensuring representation across different groups is essential but poses another problem. Last, thorough screening is imperative, as enrolling unsuitable participants might just waste resources.
AI’s impact: AI interventions can fine-tune patient profiling and establish precise inclusion and exclusion criteria. By leveraging diverse datasets and machine learning, AI ensures the accurate selection of participants, a crucial element for the trial’s accuracy and integrity. This data-driven approach improves decision-making during enrollment and facilitates adaptive design as data is continuously analyzed and adjusted in real-time.
Prolonged Enrollment and Extended Timelines
Issues: Extended patient enrollment significantly prolongs study duration, delaying results and draining resources. The duration of the study is typically based on the time span between a patient’s first and last visits, as well as the enrollment duration needed to ensure sufficient statistical power. A sample size will only be large and diverse when enough eligible participants enroll.
AI’s impact: AI interventions expedite patient enrollment by streamlining candidate identification processes. Leveraging historical data and predictive algorithms, AI accelerates patient identification, potentially shortening overall trial duration. This efficiency ensures that the trial reaches its required sample size faster and statistical power is robust.
Early Signs of Comorbidity
Issues: The controlled setting may not authentically replicate the diverse nature of real-world patient scenarios. Unexpected adverse events might be discovered only after the market launch, which calls for a more proactive approach.
Consider the case of Vioxx (rofecoxib), launched by Merck. Despite a massive phase three study with around 30,000 subjects for this non-steroidal anti-inflammatory drug, the study missed very rare cardiac side effects occurring at a frequency of around one in 100,000. The odds of capturing such side effects in a study of 30,000 or even 50,000 patients are close to zero. This stresses the need for early detection mechanisms which could identify potential issues on a smaller scale.
AI’s impact: Armed with predictive analytics and real-world patient data, AI sifts through vast datasets, analyzes patterns, and employs advanced algorithms to detect obscure signals. This strengthens patient safety and lets companies sidestep market withdrawal scenarios, preserving the integrity of clinical studies.
Issues: Pathology examinations, particularly when identifying overdose instances, have traditionally been labor-intensive and time-consuming. They require meticulous analysis and substantial resources.
AI’s impact: By leveraging advancements in machine learning and computer vision, AI significantly speeds up the analysis of overdose instances within pathology samples.
Here’s a good example. Researchers working on a project addressing overdose scenarios in chimpanzee livers manually examined liver samples, slicing them into numerous pieces to determine the timing of the overdose. They drew on a dataset of over 1 million documented images pinpointing overdose locations. To accelerate the process, a model was trained to recognize patterns through ML algorithms and vision computing and seek out a specific image out of the million instances where the overdose occurred. This approach demonstrated AI’s significant potential to enhance precision and speed in a clinical setting.
Issues: Pharmaceutical companies grapple with intricate regulatory demands and maintaining comprehensive compliance across departments. This involves meticulous documentation and strict adherence to regulations, which becomes especially challenging in heavily regulated regions. In France, for example, thorough record-keeping is mandated to extend over five years, especially in manufacturing.
Inadequately recorded changes during critical junctures pose a significant risk, potentially leading to batch failures during audits. Even with electronic batch records, integrating inputs from production, Quality Control (QC), and Quality Assurance (QA) remains indispensable, especially for products undergoing years-long development.
Another critical issue arises from data disconnection. Extracting insights from the past five years for regulatory and audit optimization takes time and effort. This disconnect extends beyond regulatory aspects, creating a gap between Research and Development (R&D), manufacturing, planning, and the supply chain.
AI’s impact: Regulatory compliance tools benefit significantly from AI integration, which offers enhanced trackability and well-documented processes. AI’s capacity to comprehend large volumes of data streamlines workflows in multiple departments. By automating routine tasks such as data entry, processing, and analysis, AI reduces the risk of human error and ensures consistency in compliance documentation.
Additionally, AI-driven predictive analytics can anticipate potential regulatory issues, enabling proactive measures. AI also facilitates real-time collaboration among departments, enabling seamless information exchange and improving overall communication. This not only eases the burden but also ensures a more efficient, error-resistant, and proactive approach to regulatory compliance in the pharmaceutical industry.
Clinical Trial Reporting and Summaries for Regulators
Issues: The reports pharmaceutical, biotechnology, and medtech companies produce during clinical trials can span anywhere from hundreds to thousands of pages. The amount of raw data, complex medical terms, and detailed descriptions of methods of interpreting results in clinical reports are overwhelming for everyone: regulators, legislators, and other stakeholders. In addition, reports from international multisite trials must comply with reporting regulations in each country.
Hence, the pharma companies must create summaries – condensed versions of clinical trial reports that communicate critical findings in a digestible and compliant format. This tedious process requires multiple collaborators and revisions.
AI’s impact: By leveraging AI-powered auto-summarization tools, companies can enhance efficiency, reduce costs, and ensure the quality of their submissions. Communicating the value of well-designed clinical trials becomes more accessible, too, especially for emerging companies. As regulatory frameworks continue to evolve, embracing AI in clinical trial reporting becomes not just a solution but a strategic advantage for the pharmaceutical industry.
At Unicsoft, we have developed a Proof of Concept for an AI-powered summarizer that can cut costs for pharmaceutical companies by 30% or more.
Common Issue at Pre-Clinical Stage: Navigating the Animal Model Dilemma
The pre-clinical stage often encounters a unique challenge – choosing the suitable animal model. As we delve into this phase, it becomes apparent that the universal saying in biology holds true: “Mice lie.” This phrase sums up researchers’ nuanced struggle, as translating outcomes from animal models to humans is often a complex and confusing process.
Issues: Different animal models present different challenges. While mice are readily available and cheap, their translational limitations call for alternatives that mimic human biology more accurately. There are multiple options beyond mice – from mini pigs to ferrets – each with its own set of physiological differences from humans. But how do we navigate the intricate web of animal models, each with its strengths and limitations, to ensure reliable pre-clinical predictions?
AI’s impact: AI algorithms can analyze diverse datasets encompassing various animal models in-depth. This helps researchers make holistic decisions, considering the model’s convenience and biological relevance to human responses.
Issues: Transitioning to primate models, recognized as a gold standard in certain cases, comes with a hefty price tag, ranging from €50,000 to €70,000 per animal. Companies must balance the need for a more human-like model with the significant financial investment involved.
AI’s impact: AI proposes a paradigm shift in experimental design. By recommending a focused approach, researchers can conduct a single, well-designed study with a broader range of animals and efficiently generate a robust dataset without having to run multiple sequential experiments.
Precision in Healthcare: The Crucial Alliance of Early Detection and Predictive Medicine
Have you ever wondered why healthcare often lags in embracing proactive measures? Do we really have to endure pain before taking action, or can we intervene earlier? Timely diagnoses do play a pivotal role in maintaining good health and saving lives. Let’s explore the challenges pharmaceutical companies face in this aspect and the AI’s transformative impact on healthcare practices.
Lack of Precision for Early Illness Signals
Issues: Developing consistently reliable indicators is a top priority that requires an ability to accurately distinguish between genuine early symptoms and misleading signals. Consider the precision challenges encountered in PSA testing for prostate-specific antigens, often plagued by reliability issues such as false positives and negatives. Diseases and conditions trigger multiple pathways, yielding various indicators and biomarkers.
AI’s impact: AI relies on scrutinizing a panel of indicators instead of depending on a single signal. This targeted approach directly addresses the need for a more precise health assessment, overcoming obstacles tied to the complexity of human biology. The goal is to create an accurate prediction model that aligns seamlessly with the intricacies of the human body.
Outdated Detection Strategies
Issues: Consider traditional tumor detection strategies that rely on the presence of tumor DNA or complete cells. These models have many inherent limitations. Searching for cancer cells or DNA in blood involves waiting for a cell to migrate or burst, yielding minuscule quantities into the bloodstream. This approach delays treatment and makes it hard to capture a comprehensive snapshot of the disease, as the obtained amounts may not represent the overall tumor dynamics.
AI’s impact: All cells, including cancer cells, produce extracellular vesicles known as exosomes. AI can detect these vesicles, providing a nuanced and accurate method for early tumor detection. By focusing on exosomes, which are more readily available in the bloodstream, AI overcomes the limitations of traditional methods. This approach effectively addresses challenges associated with conventional methods, bypassing the need for tumor DNA or complete cells.
Reactive Blood Sample Analysis
Issues: Traditional blood sample analysis operates on a reactive paradigm, where patients typically seek medical attention after experiencing symptoms, which leads to delayed diagnoses and start of treatment. This approach often compromises the experience for patients, as they must endure discomfort before receiving medical intervention.
AI’s impact: AI changes this by enabling proactive blood sample analysis. Subscribers can now experience a store-like environment, receiving biometric analyses and predictive insights into potential symptoms, which enhances the overall patient experience. AI tools can also compare markers in blood samples with a vast database. Subscribers can receive tests covering comprehensive biometric analyses and a predictive overview of potential symptoms. This way, the integration of AI ensures that patients are informed about their health in real-time and can prevent diseases before the symptoms occur or identify them early on.
Need for Personalized Medications
Issues: Traditional medical practices often neglect individual genetic predispositions, lifestyle factors, and overall health conditions when prescribing treatments. This results in ineffective interventions, prolonged recovery, and unnecessary healthcare expenses.
The shift towards personalized medicine encounters challenges beyond the molecular level. While traditional approaches mandate treating patients with the same diagnosis uniformly, the true frontier lies in understanding the unique psychological fabric of each individual. Factors such as how patients relate to medical authority, cope with pain, and confront mortality vary significantly. Collaborations between pharmaceutical companies expert in drugs, diseases, and pathological pathways and tech giants like Google, Amazon, Facebook, or others who comprehend behavioral drivers are pivotal for addressing this complex aspect of personalized medicine.
AI’s impact: I believe AI is indispensable in personalized medicine, particularly in genomic interventions. But before considering gene editing, understanding the diverse psychological responses of patients to healthcare is crucial. AI’s potential lies in deciphering behavioral drivers, contributing to a truly personalized approach in healthcare.
Beyond behavioral and genomic aspects, there’s a significant shift towards healthcare analytics. This involves identifying patterns and trends in patient data to improve interactions and outcomes. Predictive and prescriptive analytics empower healthcare professionals to anticipate potential health issues, personalize medication plans, and intervene proactively. That’s what we can truly call preventive and personalized medicine.
How to Ensure the Success of AI investments?
Now that we have explored the pharmaceutical industry’s significant challenges let’s delve into practical strategies for pharma tech companies and researchers to address these pressing issues. Entrepreneurs aspire for flawless and efficient trials and solutions but often hesitate to allocate many resources to improvements. The situation will change if they see solid proof that invested funds significantly impact outcomes or enhance the chances of success.
The Proof of Concept (PoC) approach emerges as probably the most suitable strategy for pharmaceutical companies aiming to integrate AI into their tech stacks to tackle the described challenges.
Benefits of PoC for Pharma AI
The PoC-first approach is a safety net for healthcare AI and ML products. It allows companies to validate ideas, verify technical feasibility, and identify potential issues early on. This strategy focuses on sound investments, prevents impractical pursuits, and improves return on investment.
- Technical Feasibility Demonstration: AI PoC demonstrates the technical viability of available data, algorithms, models, and technologies.
- Early Issue Identification: Recognizing potential issues early on gives companies time to reassess hypotheses and come up with alternative solutions.
- User Acceptance Gauge: PoC helps understand if the solution is compelling and allows incorporating user feedback into development.
- Data Filtering for Machine Learning: PoC verifies if there is enough accurate, complete, and unbiased data and filters irrelevant data for machine learning.
- Focused Development Lifecycle: PoC serves as a roadmap, helping maintain a pre-defined development direction without excessive expenditures.
- Updating Current Digital Solutions: Companies can assess how certain AI technologies improve existing software.
The PoC approach is a pragmatic and strategic choice, providing a risk-free environment to convince stakeholders. By focusing on sound investments and avoiding impractical pursuits, the PoC-first approach significantly improves the return on investment for healthcare AI and ML products.
The pharmaceutical industry is grappling with various challenges to nail the effectiveness and precision needed in drug development and healthcare practices. The intricacies of clinical development and regulatory compliance call for some out-of-the-box thinking. That’s where Unicsoft steps in – we have the know-how to back startups and well-established pharmaceutical companies in overcoming these hurdles. Drawing from our successful AI-healthcare solutions, including projects for pharmaceutical clients, we’ve seen firsthand the game-changing impact of AI throughout the pharmaceutical value chain.
Contact us for more information specific to your particular request.