First-in-Human Means More Than Safety: How Integrated Early Development Is Rewriting Proof-of-Concept

Insights from Matt Paterson, Chief Strategy Officer at Quotient Sciences

Matt Paterson brings more than 25 years of global experience in the biotech/pharma industry, with a focus on integrating drug product and early clinical activities to optimize the drug development process. Paterson has played a central role in advancing Quotient’s Translational Pharmaceutics® platform, which enables an adaptive approach to First-in-Human and Proof-of-Concept programs. He is a frequent contributor on topics including innovation in early development, CRO & CDMO strategies, and the use of AI in CMC and process sciences.

 

 

 

A New Reality for Early Development

Early development has always been a pressure point in drug R&D. Today, it is the pressure point, the place where programs stall, funding thins, and decisions make or break the path to Proof-of-Concept (PoC). Capital is tighter, resources are more constrained and competition is fierce. Market forces are not only influencing early development but reshaping it, with biotech/pharma companies and service businesses all looking for an edge. AI-driven discovery and data generation will continue to play a role in accelerating timelines, a trend highlighted in FDA’s 2024 AI/ML discussion paper, which notes that the volume and speed of AI-supported outputs are beginning to exceed traditional evaluation capacity. Across the industry, teams are prioritizing faster and embracing innovative development models to more efficiently progress from First-in-Human (FIH) to PoC, strengthen decision-making, and preserve momentum.[1]

 

Traditional FIH studies were designed to answer fundamental questions about the safety and pharmacokinetics (PK) of a new drug candidate.[2] Increasingly however, it has become the norm that sponsors now expect early studies to deliver much greater decision-level insight into the drug candidate’s potential, across exposure response, biomarker movement, early pharmacodynamic signals, patient-relevant dynamics, and formulation performance strong enough to meet the demands of Phase II.

 

At the center of this shift is Matt Paterson, Chief Strategy Officer at Quotient Sciences. With more than two decades working across Chemistry, Manufacturing, and Controls (CMC), product manufacturing, and early clinical research, Paterson helped build and scale Translational Pharmaceutics®, an integrated model that solves many of the coordination gaps that slow early programs. His philosophy is simple and sharp: early development should function as a continuous learning engine, not a siloed series of disconnected steps. Speed, clarity, and flexibility are more than supplemental. They are the conditions required to reach PoC with confidence.[3]

 

The Case for Integration: Eliminating White Space

Early development still operates on a legacy structure:

• a CMC department or CDMO (Contract Development and Manufacturing Organization) handles formulation development
• a Good Manufacturing Practice (GMP) compliant facility manufactures clinical batches
• drug substances and drug products are tested for stability, packaged, and shipped to different parts of the supply chain
• a clinical pharmacology unit runs early phase clinical trials in healthy subjects and patients
• CROs step in later with their own systems and processes to manage clinical data and larger trials

Individually, each group performs well. But once stitched together, their activities most often run sequentially, with hand-offs and downtime in between, creating what Paterson describes as “white space” – the costly gaps that surface when different parts of the drug development process, such as formulation, product manufacturing, and clinical operations, are managed independently and operating to their own timelines, rather than as a fully integrated program.

 

White space often shows up quietly but expensively. It is the month lost waiting for drug product to arrive at the clinical site, the slow trickle of data that reaches the development teams who need it only after the moment to act has passed, or worse still, the return trip to the formulation laboratory to manufacture a different dose strength that steals another substantial block of time. Layered across a program, each handoff insidiously drains momentum and resources. This is the structural tax built into the traditional model, an ironic penalty paid by teams following the very process meant to keep development on track. Paterson notes that these gaps are not confined to early execution and often reappear as programs move beyond FIH, when coordination across development functions becomes harder to sustain.

 

Quotient Sciences introduced Translational Pharmaceutics® in 2008 to address this cycle. The premise was straightforward: if early development stalls because key functions operate in isolation, then the model itself must evolve. Rather than treating formulation, product manufacturing, clinical pharmacology, bioanalysis, and data interpretation as sequential steps, Quotient aligned them within a single coordinated framework that allows these activities to move together.

 

Paterson frames the intention simply: “The integration of services improves decision-making while reducing time and white space.” Rather than emphasizing speed as an end result in and of itself, he points to the operational coherence that comes from teams sharing the same information, timelines, and feedback loops.

 

Instead of navigating a maze of handoffs, sponsors can step into a model built for constant learning and rapid adjustment. In practice, this gives sponsors a development path with fewer pauses and less accumulated drag from handoffs. As Paterson often describes it, “Milestones can be achieved far more efficiently, and programs maintain their momentum because development no longer stops and starts between siloed teams.” This ultimately leads to better outcomes and increases the chances of success in the clinic.

 

This integration model delivers:

• Flexibility to adjust formulation composition in real-time within a clinical study
• Better decision-making based on emerging clinical data
• Timeline acceleration of 9-12 months
• Streamlined program management and supply chain
• Cost savings in R&D spend
• Reduction in material costs (e.g., drug substance and drug product)

This approach aligns with the spirit of the FDA’s Adaptive Designs Guidance, which encourages sponsors to embed thoughtful, pre-planned flexibility into trials from the outset rather than relying on reactive fixes.[4] By operationalizing that guidance, adaptation becomes a structural feature rather than an exception.

 

Paterson emphasizes that the impact is not incremental. Timelines can compress from months to days because supply no longer dictates pace. Smaller, faster, made-to-order batches allow teams to shift with the data instead of waiting for the next manufacturing cycle. In his words, “When the manufacturing facility and clinic operate as one system, science sets the tempo. You move with the data, not behind it.”

 

This integration also requires a cultural shift across the organization. It asks teams to move beyond long-standing silos, to share information in real time, and to adapt SOPs so that deep-rooted processes don’t constrain the workflows. What started as an internal experiment evolved into a strategic advantage with Translational Pharmaceutics® being adopted by hundreds of biotech and pharma companies. It continues to influence how early development is structured and executed across the industry, as Paterson put it, “Culture, science, and agility are the pillars that make this model work.”

 

FIH Is the New Proof of Mechanism

The wall that once separated Phase I from Phase II is crumbling. What used to be a clean progression from safety exploration to efficacy testing no longer holds. Sponsors now face sharper investor scrutiny, compressed timelines for value creation, and the need for earlier proof that a molecule can survive commercially. Traditional FIH studies, built mainly to assess safety and pharmacokinetics, no longer meet the moment. Industry data show that Phase II remains the steepest drop-off in the pipeline and that only about 10 to 15 percent of assets entering Phase I reach approval, which highlights the commercial risk of entering Phase II unprepared.[5]

 

For emerging biotech companies, the stakes are even higher. They need early clarity about whether the molecule they are backing can withstand both scientific and commercial pressures. With tighter investor timelines and rising competition, any early loss of momentum can be punishing. One industry analysis shows that optimizing the pre-clinical and early clinical interface can reduce development timelines by more than 40 percent by collapsing handoffs and helping teams act sooner.[6]

 

FIH has therefore shifted from a gatekeeping safety step to the first meaningful proof of mechanism. Sponsors now expect early human trials to show tolerability, a potentially viable formulation which is scalable, mechanism or biomarker signals, clear exposure-response behavior, and actionable data to determine the next step for the drug candidate.

 

This pressure is accelerating the move toward hybrid early-phase designs. These approaches bring single ascending dose, multiple ascending dose, early patient cohorts, and exploratory Phase IIa assessments into one coordinated program. Work that once unfolded across several sequential studies can now be delivered through adaptive early human design.[7][8] As FIH studies take on greater strategic importance, sponsors are paying closer attention to how early integration carries forward once proof of mechanism is established, recognizing that decision-grade insight is most valuable when programs continue to progress without reintroducing fragmentation. In practice, this integrated approach is reflected in the core design elements outlined below.

 

The elements are:

Component	Purpose/Benefit
Early patient cohorts	Captures first signals of biological activity and therapeutic relevance
Biomarker and PD readouts	Links exposure to mechanisms and supports early proof-of-mechanism decisions
Exposure – response modeling	Guides dose selection and refines escalation strategies in real time [9]
Food-effects, Drug-drug interactions (DDI), and subpopulation analyses	Surfaces variability and safety considerations before Phase II
Formulation bridging	Enables smooth transition from fit-for-purpose to Phase II-ready drug product

This shift is no longer theoretical. It is an operational reality. Each round of funding demands a sharper and more defensible data story than the one before it. Investors want evidence that a mechanism truly works, that a formulation will hold, and that a program can advance toward key milestones without stalling. The programs that survive are the ones that generate decision-grade insight earlier, faster, and with fewer blind spots, long before traditional milestones would have revealed the truth.

 

Proof in Practice: Integrated FIH to PoC Case Studies

The power of integration becomes clearest when you see it operating under real program pressure. Paterson views these case studies not just as isolated wins, but as evidence of what becomes possible when CMC, supply, design, and clinical execution function as a single system.

 

Hereditary Angioedema (HAE)

In this rare-disease program, Quotient delivered a fully integrated SAD, MAD, and early PoC study, manufacturing clinical material in real time and adjusting supply as enrollment and data evolved. With formulation, dosing strategy, and patient supply aligned from the outset, the team moved directly from healthy volunteers into patients without losing momentum. PoC was achieved in 18 months, a timeline that would have been out of reach using a traditional multi-vendor approach.

 

Obesity

This program integrated real-time drug-product manufacturing into an FIH protocol to accelerate progression toward PoC in obese patients. Designed to balance scientific rigor with operational feasibility, the study enrolled 60 participants with BMI values between 35 and 40, refining entry criteria to appropriately manage blood-pressure thresholds and common comorbidities while maintaining subject safety. An extensive panel of pharmacodynamic and metabolic assessments – including body weight, BMI, food-intake evaluations, mixed-meal testing, Homeostasis Model Assessment (HOMA) scoring, and quality-of-life measures – provided a comprehensive view of early efficacy signals. To support retention and data integrity over the extended study duration, dietary and psychological counseling were integrated throughout treatment and follow-up, strengthening trial robustness and enabling clearer decision-making for subsequent clinical development.

 

Alagille Syndrome (Pediatric Program)

Alagille syndrome required an even more complex approach, with personalized, weight-adjusted dosing for a small and geographically dispersed pediatric population. Quotient’s real-time manufacturing and logistics operation produced more than 2,500 individualized patient packs over five years, supplying 27 sites in nine countries. Maintaining this level of precision, consistency, and responsiveness is nearly impossible without an integrated supply chain, and it ensured that no patient visit was delayed due to lack of product.

 

Together, these examples point to the same conclusion. When manufacturing, study design, and clinical execution operate as one system, the FIH-to-PoC pathway becomes far more predictable and far more responsive to the science. Paterson sees this as the clearest proof of integration’s value, saying the programs show “what becomes possible when supply and decision-making stay connected to the data instead of chasing it.”

 

Adaptive Designs: A Strategic Requirement

Adaptive designs have moved from novelty to necessity. The FDA affirmed their value in 2019 when it released guidance on adaptive trial designs and gave sponsors clear permission to plan modifications such as dose changes, cohort expansions, PK-guided escalation, arm removal, and sample-size adjustments during a live study.

 

Academic literature reinforces the point. In a 2023 review, Pallmann and colleagues described adaptive designs as prospectively planned modifications based on accumulating data, noting that they are especially effective in early-phase development where uncertainty is high and course correction is essential.[10]

 

But the industry often overlooks the second half of the equation. Adaptive designs do not work without adaptive drug product supply. If manufacturing, formulation, and logistics cannot move at the pace of emerging clinical data, an adaptive design becomes a static one. The protocol may promise flexibility, yet the operational model snaps back to fixed timelines.

 

Paterson has seen this gap many times and is direct about the consequences. He describes true adaptivity as manufacturing, supply, and clinical design moving in the same rhythm, driven by the data. This includes the ability for clinical teams to request real-time changes to drug product doses or release rates, or for real-time shipment of drug product to new clinical sites. Without that alignment, the most elegant adaptive design becomes a paper exercise. In his words, “If you cannot adapt the drug product and supply to the interim data, the design is adaptive only on paper.”

 

He reinforces this point when discussing integrated execution. Paterson notes that sponsors increasingly want to move deeper toward PoC because they need confidence that both drug substance and drug product are suitable for continued investment—confidence that depends as much on real-time manufacturing and delivery as on protocol flexibility. The effectiveness of adaptive design, he argues, rests less on statistical sophistication than on the operational infrastructure behind it. When CMC, drug-product manufacturing, supply chain, clinical pharmacology, and data sciences operate as a single governed system, teams can “manufacture, test, learn, and adjust” in step with emerging data. Without that level of integration, adaptive trials slow down rather than accelerate, undermining the very efficiencies they are meant to deliver.

 

AI Agents: The Next Evolution of Integration

AI is accelerating across the drug development landscape. FDA’s 2024 discussion paper on artificial intelligence and machine learning, along with several recent academic analyses, highlights rapid adoption of AI tools supporting toxicity prediction, dose optimization, multi-omics data integration, manufacturing control, and protocol risk analysis.[11] [12] A 2025 scoping review identified more than 140 machine-learning applications aimed specifically at clinical trial risk assessment.[13]

 

Paterson approaches AI with a practical lens. He sees the most immediate value in two areas of development. First, in optimizing clinical trials, helping teams improve trial design, patient recruitment, and data analysis efficiency and outcomes. Second, in guiding formulation development by assisting developers in selecting the best formulation and dosage form to deliver the drug molecules at the right concentration for the target patient population, giving teams an earlier line of sight into trends that would otherwise take days or weeks to detect.

 

For Paterson, the goal is not automation for automation’s sake. It is to let the experts focus on interpretation and judgment, while the AI manages the analytical weight. He describes the emerging model as one where scientists “stay at the center of decisions but have better, faster signals at their fingertips.” That shift mirrors the evolution from fixed to adaptive design, where the technology supports the tempo, but people shape the decisions.

 

AI agents are poised to serve as an additional layer of continuity, helping reduce friction in the system and giving teams earlier access to actionable signals.

 

The Five Imperatives for Modern Early Development

Paterson’s perspective on the modern early-development landscape is straightforward: the programs that win are the ones that collapse distance between science, supply, and decision-making. Across multiple interviews and case studies, he returns to the same principle. Success from FIH to PoC is no longer driven by isolated excellence within CMC, clinical pharmacology, or analytics. It comes from synchronizing them into a single, responsive system.

 

The following five imperatives capture the framework Paterson believes every sponsor should build into their early-phase strategy:

 

FIH–PoC Success Imperatives

Imperative	Key Point	Details
1. Start FIH with Phase II in mind	Early PoC	PoC begins at first dosing, not after Phase IIa
2. Integrate CMC and clinical functions early	Early Integration	The sooner these teams converge, the fewer surprises downstream
3. Embrace adaptive designs where appropriate	Efficiency & Ethics	Adaptive designs are efficient, ethical, and supported by regulators
4. Build AI into a validated, integrated environment	Embedded AI	AI delivers maximum value when embedded directly into operations
5. Invest in culture, not just capability	Team Alignment	Integration only works when teams are aligned, empowered, and operating with shared purpose

Conclusion: Integration as a Survival Strategy

Early development is changing. It is faster, more complex, and far less forgiving of missteps. Programs built on linear, compartmentalized models are struggling to keep pace. The ones that succeed are engineered for real-time coordination between science, supply, and decision-making.[14]

 

Under the leadership of experts like Matt Paterson, Quotient Sciences has built an early phase model designed for these realities. Integration, adaptive design, real-time manufacturing, and unified data form the foundation for confident PoC. Through its partnership with Biorasi, that integrated approach extends into global trial execution, patient access, and regulatory continuity, helping preserve momentum as programs transition beyond early development and into value defining stages.

 

FIH is no longer a checkpoint. It is the launchpad for confident, capital-efficient PoC. Paterson puts it clearly: “Science moves faster when people and processes are connected. When you integrate manufacturing, supply, and clinical execution, you remove the delays that used to be accepted as part of the process. That is how you get to better decisions sooner.”

 

 

Note: Biorasi and Quotient Sciences maintain an established strategic partnership. The authors developed all content in this article independently.

 

[1] Nature Reviews Drug Discovery (2023)

[2] Wong, Chi Heem, et al. “Estimation of Clinical Trial Success Rates and Related Parameters.” Biostatistics, vol. 20, no. 2, 2019, pp. 273–286.

[3] Quotient Article Summary: First-in-Human to Proof-of-Concept. Internal document, 2025.

[4] U.S. FDA. Adaptive Designs for Clinical Trials of Drugs and Biologics: Guidance for Industry. 2019.

[5] Hay, Michael, et al. “Clinical Development Success Rates for Investigational Drugs.” Nature Biotechnology, vol. 32, 2014, pp. 40–51.

[6] https://www.mckinsey.com/industries/life-sciences/our-insights/fast-to-first-in-human-getting-new-medicines-to-patients-more-quickly?utm_source=chatgpt.com

[7] Pallmann P. et al., BMC Med Res Methodol. 2023.

[8] Chow, Shein-Chung. “Seamless Phase I/II Designs.” Journal of Biopharmaceutical Statistics, 2014.

[9] https://pmc.ncbi.nlm.nih.gov/articles/PMC10260347/?utm_source=chatgpt.com

[10] Pallmann P. et al., BMC Med Res Methodol. 2023.

[11] U.S. FDA. Using Artificial Intelligence & Machine Learning in the Development of Drug and Biological Products. Discussion Paper, 2024.

[12] Serrano, David R., et al. “Artificial Intelligence Applications in Drug Discovery and Development.” Journal of Drug Delivery and Therapeutics, 2024.

[13] Katsimpras, Georgios, et al. “A Scoping Review of Artificial Intelligence Applications in Clinical Trial Risk Assessment.” npj Digital Medicine, vol. 8, 2025.

[14] DiMasi et al., Tufts CSDD (2016–2022)