ProSciento, Inc.

Today is Clinical Trials Day, and we're taking a moment to reflect on the impact of what is considered to be the first randomized clinical trial.   On May 20, 1747, Dr. James Lind started the world’s first clinical trial. He hypothesized there was a connection between citrus consumption and the incidence of scurvy. Nearly 300 years later, the spirit behind that work lives on in every protocol written, every site activated, every participant enrolled, and every data point carefully collected and analyzed.   Every clinical trial represents an opportunity to improve the lives of patients and families and to redefine health in the future. To the investigators, coordinators, nurses, patients, and research teams who show up every day to make trials happen – thank you. This work is detail-driven, deeply human, and matters more than words can capture. It is the contribution of each participant and the dedication of everyone at study sites, sponsors, and CROs that make this possible. We're proud to support the science that impacts clinical trial development and hope for patients . Here's to the people who make it possible. 

Every study protocol is built on a set of underlying assumptions e.g. related to:  – Population identification  – Endpoint measurability  – Patient burden and adherence  – Site capability and workflow    In metabolic studies, these assumptions often involve whether assessments, visit schedules, fasting requirements, procedures, and safety monitoring can be executed consistently across sites and participants.    For example, a protocol may assume that postprandial glucose and insulin measurements can be collected at tightly defined timepoints following a standardized meal. In practice, delays in meal administration or sample collection at the site level can shift these timepoints, introducing variability in the data that’s captured and later analyzed.    When these assumptions are not validated early against site-level operational execution, they begin to break down during study conduct as:  – Protocol amendments   – Delays   – Increased data variability between sites and cohorts    These breakdowns introduce inconsistency in how the protocol is executed across sites, which affects data reliability and interpretability.    Addressing this requires early, data-informed evaluation of how the protocol performs under real-world site workflows and constraints. 

In metabolic clinical development, what a study can prove is often shaped long before the first patient is enrolled.    Protocol design is where scientific rationale becomes an executable study framework, aligning biology, population definition, endpoints, measurement strategy, and operational feasibility.    When those elements hold together, studies are better positioned to generate interpretable, decision-relevant evidence. When they do not, uncertainty can be introduced early and carried through execution.    In our latest article, Alejandra Macias, MD, Senior Director of Medical Sciences at ProSciento, explores how protocol design connects clinical rationale to study execution, and why early alignment is critical to what a study can ultimately demonstrate.    Read more: https://lnkd.in/g8e4Bk_J

ProSciento was honored to join LSK Global PS in Seoul for the launch of our strategic collaboration to support metabolic clinical development across Korea and the broader Asia-Pacific region. Hosted by LSK Global PS, the event brought together client partners and industry leaders for a timely discussion on the evolving needs of metabolic clinical development. As research into obesity, diabetes, MASLD/MASH, and related metabolic conditions becomes more complex, sponsors need partners who can bring together scientific expertise, regional insight, and operational execution. This collaboration brings LSK Global PS’s regional CRO platform, local regulatory knowledge, and established presence in Korea together with ProSciento’s deep expertise across the metabolic continuum. Together, we aim to support sponsors advancing complex metabolic and cardiometabolic programs with greater scientific continuity, regional reach, and operational efficiency. Thank you to LSK Global PS for hosting an outstanding event and for such a strong foundation to build from. We are excited about this partnership and look forward to the work ahead.

Baseline dynamics, not the diagnosis label, drive variability, feasibility, and interpretability in early-phase metabolic development. Diabetes is often discussed as a single indication. In practice, it spans multiple glycemic phenotypes, differences in baseline control, glycemic variability and glycemic excursions, treatment context, and resulting risk. That heterogeneity matters because early-phase designs can quietly assume a uniform baseline when the physiology (and standard-of-care context) is anything but uniform. A phenotype-aware lens surfaces three clarifications: - Who the study is truly for (beyond a diagnosis label) - What baseline dynamics and treatment context are doing to variability and interpretation (including standard-of-care and prior therapies) - How measurement strategy should reflect dynamics, not just static snapshots When dynamics matter, “average change” can hide meaningful differences in stability, deviations, and response patterns. The goal isn’t complexity; it’s reducing hidden assumptions before they surface as noise, friction in feasibility, or interpretability risk.

We’re pleased to welcome Daniel Smith to ProSciento as Executive Vice President of Global Business Development. Daniel brings more than 25 years of experience across healthcare and life sciences, with deep experience in strategic partnerships and business development across the clinical development landscape. He has held senior leadership roles at EVERSANA, PPD, Worldwide Clinical Trials, and ICON. At ProSciento, Daniel will lead global business development efforts, focusing on partnerships, market expansion, and strategic growth initiatives that support our metabolic clinical development work. “ProSciento has built its reputation based on our uncompromising focus on the science of the metabolic continuum, the patient populations we serve, and the clinical development decisions of our clients that matter most,” said Marcus Hompesch, MD, CEO of ProSciento. “Daniel brings deep commercial experience and a strong understanding of how strategic relationships and partnerships with our clients can support that work. We’re pleased to welcome him to the leadership team as we continue to strengthen our position in metabolic clinical development.” “I’m excited to join ProSciento at a meaningful point in its growth,” said Daniel Smith. “The company has a clear scientific focus, a strong reputation in metabolic clinical development, and a differentiated approach to partnering. I look forward to working with the team to build relationships that support ProSciento’s next phase of growth.” Please join us in welcoming Daniel to ProSciento.

Biomarkers by Therapeutic Area: Measurement that Matches Phenotype Biomarker integration only helps when it’s tied to phenotype intent, supporting interpretability rather than adding panels by convention. Across metabolic diseases, the same diagnostic label can map to different underlying physiologies, so the measurement plan should align with the phenotype's intent, not convention. A practical way to think about it: · Match the biomarker to the question: Are you trying to support a mechanism, stratify heterogeneity, or interpret clinical change? · Be cautious with “one-size-fits-all” panels: Obesity, glycemic disease, and steatotic liver disease often require different measurement emphases, even when populations overlap. · Plan interpretability up front: Choose readouts and timing that can separate expected baseline variability from treatment-related change. When measurement aligns with the phenotype, studies are easier to interpret, and decisions are easier to defend.

Study conduct can drive behavior-driven outcomes, so a placebo response should be built into the design, not discovered late. In metabolic development, outcomes often reflect both physiology and behavior. Study participation itself can shift routines, adherence, and expectations, and those effects are not evenly distributed across populations or sites. Two places to plan for it early: · Study conduct, visit cadence, coaching intensity, and assessment burden can shift behavior-driven outcomes and persistence · Interpretation, meaningful placebo movement can blur signal detection unless baseline context and operational variation are pressure-tested up front The goal isn’t to “eliminate placebo.” It’s to design with it in mind so the study runs smoothly and the results are easier to trust.

“Lean mass” is frequently used as a proxy for muscle in obesity trials—but it does not exclusively reflect muscle tissue. As regulatory expectations in obesity development evolve, there is increasing focus not just on weight loss, but on preservation of muscle tissue and its function. It is important to understand that DEXA and MRI do not measure the same thing. • DEXA: scalable, but aggregated DEXA provides estimates of fat mass and fat-free mass across the whole body. It’s efficient and widely used. But fat-free mass is not synonymous with muscle tissue. It includes water, organ mass, and other non-adipose components. During weight loss, shifts in hydration can change fat-free mass estimates, making “lean mass loss” difficult to interpret in isolation. • MRI: compartment and tissue-specific measurement MRI allows for a more direct assessment of adipose tissue and muscle tissue across compartments. This includes: • Distribution of adipose tissue across regions and organs • Muscle volume alongside lipid content within muscle Why this matters? Two patients can lose the same amount of weight—or show similar changes in fat-free mass by DEXA—and reflect different underlying changes in adipose and muscle tissue. The question is not just how much weight or “lean mass” is lost. It’s what tissue is changing, and how that shapes interpretation.

Continuous glucose monitors add powerful insight to metabolic trials, but they also introduce additional regulatory and operational responsibilities. Continuous Glucose Monitoring in Metabolic Clinical Trials: Scientific Opportunity and Regulatory Considerations Continuous glucose monitors (CGMs) expand how glucose dynamics are measured in metabolic clinical development. Endpoints such as HbA1c and intermittent glucose measurements capture glycemic status at defined points in time. CGMs generate continuous, high-frequency data (~288 readings per day), enabling evaluation of glucose patterns across the full daily metabolic cycle. This enables evaluation of additional metrics such as: 1. Time in Range (TIR) 2. Time Below Range (TBR) 3. Glycemic variability 4. Nocturnal hypoglycemia These measures can help characterize glucose dynamics at a level that traditional point measurements cannot fully capture. But incorporating CGMs into clinical trials is not purely a biomarker and measurement decision. It requires alignment across study design, endpoint strategy, and operational planning. Three areas require alignment: 1. Endpoint definition and analysis planning CGM-derived metrics must be prospectively defined, including endpoint hierarchy, analysis populations, and handling of missing or interrupted data. 2. Data integrity and regulatory compliance Continuous device-generated data introduces requirements for traceability, audit readiness, and alignment with ICH-GCP across data flows, platforms, and vendors. 3. Operational execution Device logistics, sensor replacement, vendor oversight, and site and participant training must be integrated into study conduct to ensure consistent data capture. As metabolic clinical development becomes increasingly aligned to biology, CGMs are incorporated into study designs to support more detailed characterization of glucose dynamics and treatment response. At ProSciento, CGM integration is evaluated within trial design across endpoint strategy, protocol development, and operational execution, with emphasis on interpretability and regulatory readiness.