Improving Clinical Trials

FDA just changed the rules of the oncology game: survival is the scoreboard. For years, companies leaned on surrogate endpoints—progression-free survival, tumor shrinkage, biomarkers. Flashy. Fast. But not always life. Now the FDA’s draft guidance makes it clear: → Overall survival (OS) should be the primary endpoint in randomized cancer trials when feasible → Even when it’s not, sponsors must collect and prespecify OS data—because safety and efficacy live there → Design fixes are coming for crossover, unequal randomization, and long-term follow-up Why it matters: as FDA and AACR leaders put it, we’ve seen cases where progression-free survival looked great—but patients didn’t live longer. Sometimes, they lived shorter. That’s unacceptable. This is a reset. Biotechs, CROs, CDMOs: if your trials don’t put survival front and center, you’re not future-proof—you’re playing the wrong game. The only metric patients care about is time. Is your company ready to measure up?

The FDA's Operation TrialBlazer is a serious and welcome initiative — streamlining IND submissions, enabling real-time data sharing, and continuing the transition away from less-than-predictive animal models. The backdrop is stark: At 39%, China surpassed the United States in total registered clinical trials in 2024. Reversing that matters. But I want to make a more ambitious argument: the goal should not be to regain the ground we lost. It should be to leapfrog it. AI applied systematically to high-content clinical data can change the fundamental economics and biology of what a clinical trial can do. Plank 1: Patient Identification. Clinical trial recruitment is largely a manual, geography-dependent process — patients near an academic medical center get access; other patients largely don't. AI models operating on large databanks of de-identified standard-of-care data — blood labs, imaging, EHR records — can identify likely candidates at scale, across health systems, independent of geography. The result: not only faster recruitment, but a more representative patient population that actually looks like the people who will receive the drug. Plank 2: Patient Selection. A trial that enrolls a heterogeneous population and waits for signal to emerge is a blunt instrument. AI models can identify likely responders — patients for whom a mechanism is most likely to work — and rapid progressors, whose disease moves fast enough that a treatment effect becomes visible quickly. Enriching trials for both can hugely reduce the required sample size and duration. A trial that would have taken five years and two thousand patients can be powered with a fraction of both. Plank 3: Quantitative Biomarkers. Clinical endpoints — functional scores, survival, hospitalization — are the right ultimate measures, but crude, slow, and hard to power. Viral load transformed the trial feasibility of HIV antiretrovirals; without it, these life-saving drugs would have taken decades longer. Neurofilament light chain is doing something similar for ALS. These are the exceptions. AI applied to high-content data can systematically identify new quantitative endpoints across disease areas where we currently have none. The real prize is not catching up — it is building the data infrastructure and regulatory frameworks that allow AI to transform what a clinical trial can do. That means facilitating access to anonymized, high-content standard-of-care data at scale, and a framework for AI-assisted patient identification that protects privacy while enabling reach. It means providing regulatory guidance and active encouragement for AI-derived endpoints and biomarkers. The U.S. has the scientific talent, the computational infrastructure, and the AI capabilities to lead this transition. Do this, and we don't just run faster trials — we run trials that find the right patients, measure the right signals, and deliver answers the current paradigm cannot. That is what patients are waiting for.

Clinical Research Needs a Reality Check, R3 Is Here Wake-Up Call: The new ICH-GCP R3 guidelines just dropped, and if you’re still running trials like it’s 2010, you’re already behind. R3 demands risk-based approaches, decentralized elements, and true patient-centricity. Yet, the industry keeps dragging its feet. Why? Because disruption is uncomfortable. What Needs to Change, Now: 1. Stop Wasting Time on Outdated Monitoring R3 prioritizes risk-based monitoring (RBM). If you’re still obsessed with 100% SDV, you’re part of the problem (minus some early phase oncology- if you know, you know). Solution: CRAs need to evolve into data-driven strategists. Equip yourself with skills in data analytics and centralized monitoring tools to spot trends before they become risks. Learn to read the signals, screen failure rates, dropout patterns, and query spikes tell a story. CRAs who identify these trends early will be the ones leading trials, not just monitoring them. 2. Decentralized Trials Are the Standard, Not a Nice-to-Have Still forcing patients into endless site visits? R3 says adapt or get left behind. Solution: Break into roles shaping the future: - Decentralized Trial Coordinator - Telehealth Study Manager - Remote Monitoring CRA 3. Patient-Centricity: Less Lip Service, More Action R3 is clear: trials must fit patients, not the other way around. Solution: Target roles like: Patient Engagement Lead, Design protocols around real lives. Your Next Move: Master R3: Knowledge of ICH-GCP R3 guidelines = competitive advantage. Target Future-Proof Roles: RBM specialists, DCT experts, and patient-centric strategists are the future of research. Think Like a Trendspotter: The best CRAs don’t just report data, they predict the next move. The Real Question: Are you disrupting the industry, or waiting to be replaced by those who will?

Throughout my career in cancer research, I’ve been humbled to witness the incredible scientific and clinical journeys of numerous therapies, from early concepts to potentially life-extending medicines for patients. As I reflect on this at the start of the new year, I’m reminded of one constant; oncology drug development is not always linear. At times, twists, turns and setbacks are familiar territory for anyone involved in cancer drug development. What’s important for our continued progress is that we learn, iterate and continue to build on the knowledge gained, through a scientific and data-driven approach. Every well-constructed study provides valuable information that informs future research. Our progress for patients is measured by how we respond to obstacles in order to define our future path.     At GSK, we're excited about our data- and technology-enabled approach to Oncology Research & Development.    Here are some key reflections and learnings, from experiences, that we're applying, and have documented in published manuscripts, as we set out to achieve ambitious goals for 2025 and beyond:    1. Study Optimization: The oncology landscape is ever changing, and we must continue to evolve our clinical trial designs accordingly. This includes evaluating appropriate endpoints, statistical analysis plans and length of follow-up, as well as selection and structure of comparator arms and combination therapies.    2. Iterative Learning: We consistently apply unique insights gained from ongoing early phase research and data generated from late stage trials —not only our own, but across the scientific community—to the next steps of drug development. Building on our existing, collective knowledge, we’ll be best positioned to accelerate discovery and development timelines, bringing critical innovation to patients faster. 3. Relentless Pursuit: By pursuing diverse therapeutic modalities, combinations and robust patient selection strategies, we increase our probability of timely success and, ultimately, the likelihood of delivering effective therapies for those patient segments who need them the most.    While research is inherently complex, by charting a path forward—that is scientifically rigorous & consistently informed by data and unique insights—we can optimize our efforts to achieve more rapid and efficient innovation for patients in areas of high unmet need. I'm excited by what 2025 will bring.   #AheadTogether #OncologyRnD #DrugDevelopment Pralay Mukhopadhyay, PhD Joanna Opalinska Prani Paka Eric Richards Ramon Kemp Ali Çimen Zeshaan Rasheed

I just watched an AE lose a $1.2M deal after running a "successful" product trial that the prospect LOVED. After 8 weeks of work, the CFO killed it with five words: "Let's try our current vendor." This happens because most reps treat trials as product demos instead of what they actually are: RISK ELIMINATION EXERCISES. After analyzing 200+ enterprise sales cycles at companies like Salesforce, HubSpot, Thomson Reuters, and Workday, I've identified the exact framework that separates 80%+ trial conversion rates from the industry average of 30%. Here's what most reps get wrong: They skip qualification and jump straight into the trial. Big mistake. Before any trial, ask these 3 questions: → "What happens if you don't solve this problem in the next 90 days?" → "How have you tried solving this before?" → "Who else is affected by this problem?" These eliminate 68% of unqualified trials before they start. Next, define success upfront: → Technical requirements that must work → Business metrics they expect to see → Timeline for implementation → User adoption patterns needed Get confirmation: "Just to confirm, if we demonstrate these criteria, you'd be ready to move forward with purchase by [date]. Correct?" Map every stakeholder: → Technical buyers (include every trial user) → Economic buyers (CFO/budget holder) → Political influencers (who can kill deals) → Current solution advocates (who benefits from status quo) For each person, document their personal win/loss scenarios. Have legal review agreements BEFORE starting trials. "We typically have legal review the agreement structure ahead of time so there are no surprises later. Would you be open to having them review a blank agreement while the trial is running?" Finally, handle the current vendor objection upfront: → "Have you discussed these challenges with your current vendor?" → "What was their response?" → "What specific capabilities do they lack?" Document these answers to build your business case. Results from this approach: ✅ Trial conversion: 32% to 83% in 60 days ✅ Deal size increased 40% ✅ Sales cycle shortened 37% ✅ Forecast accuracy improved 92% ✅ 43% less time on unsuccessful trials Stop running trials. Start running risk elimination exercises. — Sales Leaders! Your reps don’t need another training. They need a Revenue OS™. Check this out: https://lnkd.in/ghh8VCaf

Stop treating your CRO like a vendor - and start treating them like a partner. CROs aren't just service providers you hire and forget. Instead, they are strategic partners who can make or break your study success. Instead of: "We hired them to execute our plan."   Think: "We partnered with them to achieve our shared goals." But - what does make a sponsor-CRO relationship successful? Trust: The basis for solving problems together. When a site is struggling with enrollment, the partners brainstorm solutions as a team rather than playing the blame game. Transparency: The best sponsors give their CROs full context and not just task lists. The better I know the sponsor's goals, the better I can manage (my/your) our study. The partners have a common goal. Flexibility: We need to acknowledge that protocols may change, timelines shift, and unexpected challenges arise. The better the risk assessment, the higher the accepted need for flexibility. Respect: We must not forget that success is collective. Partnering on the sponsor side means: Choosing CROs based on capability and cultural fit, not just the lowest bid.  Investing time in relationship building, not just contract negotiations. And providing regular feedback, not just when problems arise. And CROs? They should think like owners, not contractors. They bring solutions and consult in case of challenges. They communicate proactively, especially when things go wrong. Let us be honest: Most CRO professionals entered this industry for the same reason as pharma, biotech or medtech professionals: Namely to help bringing life-changing treatments to patients. What does partnership look like in your sponsor-CRO relationship? #ClinicalResearch #SponsorCRO #Partnership #ClinicalTrials #Collaboration 

Patient‑centricity in healthcare has grown up. And that’s a good thing.   In healthcare and life sciences, we’re moving from engagement to co‑creation.   Patients are no longer being “looped in” late. Co‑creation isn’t an occasional workshop anymore—it’s becoming part of trial‑design muscle memory. When patient input is embedded early, clinical trials see ~25% faster enrollment and significantly fewer late‑stage amendments. Decentralized, patient‑friendly designs are also delivering ~20% higher retention. That’s impact—not intent.   The second shift is equally important: we’ve moved from good intentions to measurable outcomes. Patient experience is now treated as an operational lever. It’s measured, tied to KPIs, and discussed alongside timelines, cost, and risk. That signals true maturity.   The third evolution is how we use technology. We’re seeing a move from digital tools for novelty to responsible AI with purpose—designed to reduce patient burden, not add complexity. Simpler protocols. Smarter scheduling. Better listening to patient signals.   Taken together, this marks a fundamental change in mindset. Patients are being recognized for what they truly are— co‑experts in healthcare design, not just end users.   The question for leaders is no longer why patient partnership matters. It’s how deeply we’re willing to embed it into how we work, decide, and build. #PatientCentricity #PatientExperience

Decentralized clinical trials aren’t just an innovation—they’re a lifeline. For #patients managing rare or chronic conditions, traveling to clinical sites can be exhausting, costly, or downright impossible. At Rare Patient Voice, we hear it all the time: logistics are one of the biggest barriers to participation. By bringing the trial to the patient—whether through telehealth, local labs, or at-home visits—we reduce those barriers and increase access. This approach doesn’t just speed up recruitment. It supports real lives and real stories, especially for patients in underserved or rural communities. Because when trials are designed around the people they aim to help, everyone wins. Let’s keep building research that meets patients where they are. Literally.

I’ve now ran over 100 pilots (trials) at Gong. With a win rate of over 90%. 4 biggest lessons. 1. 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 Never begin a pilot without executive alignment. Ideally, the economic buyer has already been engaged through demos / the evaluation. If not, this is a great opportunity to get them looped in as a ‘give / get’ before starting. “Before approving a pilot, we require exec alignment. I’ve learned it’s much easier to ask for 20 minutes upfront and all be aligned, than 50K at the end. How can we loop ___ in?” 2. 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 𝐂𝐫𝐢𝐭𝐞𝐫𝐢𝐚 Before beginning a pilot, align on success criteria with the team + economic buyer. Always come ready with criteria proposed to help guide them as to what they should be looking to prove. Keep them simple. Under promise, over deliver. I also use the time to uncover additional risk. “Say we nail all the success criteria, you love the pilot, but the team decides not to sign on (date). What are the most likely 2 reasons why?” 3. 𝐌𝐮𝐭𝐮𝐚𝐥 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 𝐏𝐥𝐚𝐧𝐬 Create a mutual success plan that outlines the success crtieria, sessions, pilot resources, etc. and share it with your POC to encourage editing. I have 3 lines that include - security, legal, and signer. 4. 𝐒𝐜𝐡𝐞𝐝𝐮𝐥𝐞 𝐚𝐥𝐥 𝐬𝐞𝐬𝐬𝐢𝐨𝐧𝐬 𝐮𝐩𝐟𝐫𝐨𝐧𝐭 If your pilot / trial process includes trainings, insights, check-ins, get them scheduled in bulk. Never have to worry about grabbing a next meeting then. Key to all 4... having a great, repeatable template to guide the buyer. Snag my (free) mutual success plan: https://lnkd.in/gGDQKgfC 🦙🦙🦙