For small biopharma companies, time is money, and the clinical development plan (CDP) must identify and leverage new and emerging technologies to reduce costs and accelerate the clinical program.
In Part 1 of our Clinical Development Plan blog series, Optimizing Your Clinical Development Plan: Strategies for Biotech Success, we explored how a well-constructed CDP serves as a strategic roadmap, guiding investigational products from preclinical research through clinical trials and regulatory milestones. This approach helps biopharma companies anticipate challenges, align cross-functional expertise, and maximize the probability of success, particularly when supported by experienced clinical development services teams. As the development landscape continues to evolve, small and mid-size biopharma companies face mounting pressure to deliver results faster, more efficiently, and often with limited resources.
Part 2 of this blog series turns to the new frontiers shaping modern clinical development. Here, we examine how emerging technologies are transforming the way clinical programs are designed and executed. Key innovations driving this transformation include:
- Artificial intelligence (AI), to enhance data analysis, patient recruitment, and protocol optimization
- Virtual and decentralized trials, expanding patient access and enabling real-time data collection through wearables and telemedicine
- Advanced modeling and simulation, supporting evidence-driven study design and risk reduction
- Master Protocols: Enables efficient evaluation across multiple studies.
- Adaptive Trial Designs: Allows flexible, data-driven study modifications.
- Biomarker Development: Improves patient targeting and efficacy assessment.
- Real-World Evidence: Supports regulatory submissions and reduces trial size.
By integrating these innovations into the CDP, small and mid-size biopharma companies can reduce costs, accelerate timelines, and improve the likelihood of success, all while navigating the complexities of funding and regulatory expectations. This installment provides practical recommendations for leveraging these tools to build a more agile, data-driven, and patient-centric clinical development strategy.
Emerging Technologies Reshaping the Clinical Development Plan
- AI Enhancement: At the discovery stage, AI-powered predictive tools are expanding the universe of druggable targets. Machine learning algorithms can rapidly analyze vast biological datasets to identify disease-causing proteins and genes, while advanced platforms like AlphaFold predict protein structures and drug-target interactions with unprecedented accuracy. This capability is particularly valuable for targeting molecules previously dismissed as “undruggable”, enabling researchers to explore chemical spaces that were inaccessible through traditional methods. The impact is quantifiable: AI-discovered drugs in early clinical trials demonstrate success rates of 80-90%, compared to 40-65% for traditionally discovered drugs. Beyond the discovery phase, AI is also influencing clinical development planning. Predictive modeling tools can refine protocol design and support feasibility assessments. They can also improve enrollment forecasting by analyzing historical trial performance and real-world data. In practice, that means anticipating enrollment challenges before first patient in (FPI), rather than adjusting timelines after delays occur.
- Virtual Tools Enabling Decentralized Clinical Trials: A silver lining to the COVID-19 pandemic measures has been the rapid advancement of improved virtual/decentralized clinical trial (DCT) capabilities. Technology and infrastructure have significantly improved as companies responded to the need for conducting clinical research while minimizing face-to-face interactions. Mobile phones and apps, wearable technologies such as smart watches, enhanced tools for virtual vital sign and cardiac monitoring, smart pills, electronic patient-reported outcomes (ePROs), and telemedicine platforms now make virtual clinical trial visits, and even fully virtual Phase 1 clinical trials, feasible. FDA recently released a final Guidance for Industry regarding the Use of Digital Health Technologies for Remote Data Acquisition in Clinical Investigations providing sponsors with a framework for leveraging these tools to gather clinical data effectively. Platforms enabling electronic consents and clinical outcome assessment, along with mobile medical technicians, facilitate blood and urine sample collection at participants’ homes. Whether part of a fully virtual or a hybrid trial, these approaches expand the geographical reach and patient pool of a study, reduce participant burden, accelerate trial enrollment, and reduce overall operational costs.
Importantly, the reduced visit burden can lead to improved patient retention in such studies, while real-time data capture allows for earlier intervention in cases of protocol deviations. Virtual trials can also facilitate post-marketing studies and long-term follow-up for cell and gene therapy trials. While some in-person interaction with study sites remains necessary, data capture can come straight from devices and central labs, reducing the need for costly and time-consuming data monitoring. With each new digital innovation, decentralized and virtual trials are moving from possibility to practice, steadily reshaping how clinical research reaches and retains patients, as reflected in recent FDA guidance on decentralized trial elements. - Modeling and Simulation: Pharmacokinetic (PK) and Pharmacodynamic (PD) modeling are increasingly leveraged throughout clinical development to:
- Identify optimal dosing regimens for Phase 2 studies, maximizing efficacy and minimizing toxicity
- Assess the likely effect of a product vs. existing therapies, supporting comparative effectiveness decisions
- Predict drug exposure in special populations (e.g., those with renal impairment, children, elderly), and extrapolate adult data to pediatric populations
- Accelerate development for new indications and new routes of administration
- Reduce the likelihood of Phase 2 and Phase 3 trial failures by enabling data-driven, risk-mitigating decisions
Regulatory agencies increasingly encourage the use of modeling and simulation to inform dose selection, trial design, and labeling, making these approaches fundamental to modern drug development. For more detail on how mechanistic modeling supports faster, evidence-driven decisions, see our blog Fast-Tracking Drug Development: Role of Mechanistic PK/PD Modeling.
- Master Protocols: The FDA’s recent guidance on Master Protocols (explored in more detail in Allucent’s analysis of Master Protocol adoption trends) provides recommendations for designing these complex trials. Use of a Master Protocol allows incorporation of a number of individual sub-studies within the larger context of the Master Protocol and applies whether testing multiple drugs for one disease (umbrella or platform trial) or evaluating one drug for multiple indications (basket trial). A Master Protocol can expedite the clinical program by testing different formulations, routes of administration, molecular targets, drug combinations, or different diseases/disease sub-types within a single protocol, moving a program more quickly to the optimal subject population and dose regimen when compared to traditional standalone trials. This approach may be particularly useful in a clinical landscape with multiple existing standards of care and in identifying the optimal combination treatment. Where sites are involved in multiple sub-studies, small biotech net the benefit of a single start up and contract negotiation. Altogether, a Master Protocol’s expanded clinical evaluation can determine “winners” saving protocol development time and minimizing trial resources.
- Adaptive trial designs: Similar to master protocols and modeling, adaptive trial designs are intended to shorten development time by evaluating/re-evaluating trial data during an ongoing trial to refine key design elements such as dose levels, dose regimens, and study population. Early phase oncology protocols are commonly using BOIN designs to move more rapidly through dose escalation and dose optimization while reducing the risk of overdosing subjects. Overall adaptive trial designs can reduce development timelines by faster identification of ineffective doses or subject subgroups (based on biomarkers), supporting a seamless transition from Phase 2 to Phase 3 within a single trial, or earlier determination if a trial should be terminated based on interim data. Efficient use of adaptive trial designs requires input from a biostatistician who is well experienced with adaptive design methodologies, and confirmation of the planned approach by regulatory authorities.
- Biomarker Development: Early identification of clinically relevant biomarkers can identify the optimal patient population for a therapeutic, helping ensure the best efficacy evaluation. Properly validated biomarkers directly related to the effect of a therapeutic can also serve as surrogate endpoints, setting the stage for an accelerated approval.
- Real World Evidence: Leveraging real-world data (RWD) to generate real-world evidence (RWE), especially in rare diseases, may reduce the number of clinical trials in the development program, reduce trial sizes, and even eliminate a control group. Fewer subjects mean a shorter clinical program. Since 2017, the FDA has released 8 Guidance for Industry documents related to the use of RWD/RWE to expedite development and support marketing applications. Recent rare drug approvals leveraging RWE include IWILFIN (eflornithine), where approval was supported by a single non-randomized externally-controlled Phase 3b trial in 105 subjects comparing subject outcomes to a historical benchmark derived from a large external control arm derived from controlled trial data; and Veopoz (pozelimab-bbfg) approved for the treatment of the rare CHAPEL disease based on data from a single-arm study in which 10 subjects’ pre-treatment data was compared to outcomes during the trial. Real world data sources include electronic medical records, electronic data from previous trials, data from wearables, cooperative groups, and disease registries. Early interaction with regulatory authorities is critical to ensure the proposed RWD source and the granularity of the available data is fit for purpose.
These innovative approaches, from AI-driven trial design to decentralized clinical models and real-world evidence, collectively enable biopharma companies to compress timelines and reduce costs. However, even with optimized development strategies, the financial demands of clinical programs remain substantial. Strategic funding partnerships become essential to sustain momentum through these phases.
Funding Clinical Development
Therapeutic product development is an expensive business, with cost estimates ranging up to $1.3 billion to take a product from R&D to marketing approval. Small biopharma companies can benefit from grants for early phase studies, including NIH Small Business Innovation Research and Small Business Technology Transfer Research programs and FDA Orphan Drug grants. Leveraging these outside funding opportunities can provide the early phase results needed to position an asset for acquisition or to link up with partners able to support further development.
Conclusion
As emerging technologies continue to reshape clinical development, biopharma companies have a unique opportunity to build smarter, faster, and more resilient programs. Integrating AI-driven insights, decentralized trial capabilities, advanced modeling, innovative study designs, and real-world evidence into the CDP helps reduce operational burden, strengthen clinical decision-making, and accelerate progress toward key milestones. But even the most efficient strategies require sustained investment. By pairing these scientific and operational innovations with thoughtful funding pathways and early regulatory engagement, small and mid-size biopharma companies can position their programs for long-term success and improve their chances of delivering meaningful therapies to patients.
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