Improving Clinical Trials

🔥 7 Challenges Slowing the ADC Revolution 🔥 💡 The ADC Boom: ADCs are the hottest ticket in oncology, blending antibody precision with chemotherapy’s punch. With 11 FDA approvals, 378 clinical candidates, and a market set to hit $30B by 2030, these therapies promise to redefine cancer care. ⚠️ BUT… Hidden Challenges Lurk: While stars like Enhertu (T-DXd) and Trodelvy (SG) grab headlines, 150+ ADCs have failed, and even approved drugs face toxicities, resistance, and delivery flaws. The gap between hype and reality is widening. 👇 Here’s the unfiltered breakdown of 7 make-or-break challenges the industry MUST solve: 1️⃣ TOXICITY TRAP 🚑 A) >90% of ADC patients face adverse events (46% severe). B) Payloads like auristatins (neuropathy) and maytansinoids (liver toxicity) drive “platform toxicities.” Example: ARX788 (HER2 ADC) halted trials due to 46% keratitis rates. 2️⃣ LINKER INSTABILITY ⚖️ A) 50% of maleimide-based ADCs (e.g., T-DXd) shed payloads in blood, binding to albumin. B) SG’s carbonate linker releases SN38 in <24h, raising toxicity risks. 3️⃣ TUMOR UPTAKE FAILURE 🎯 A) <1% of ADC doses reach tumors. PET scans show most ADCs pool in liver/spleen. B) Shocker: Only 0.9% ID of ⁸⁹Zr-trastuzumab accumulates in HER2+ tumors. 4️⃣ BIOMARKER BLIND SPOTS 🔍 A) HER2 PET imaging reveals 26% of “HER2+” patients are PET-negative, with 3x shorter survival on T-DM1. B) TROP2 ADCs like SG show no OS benefit in Phase III lung cancer (EVOKE-01). 5️⃣ CROSS-RESISTANCE RISKS 🔄 a) Real-world data: Sequencing TOPO1i ADCs (SG → Dato-DXd) cuts PFS from 7.3 → 4.4 months. B) China study: 2nd-line same-payload ADCs drop ORR to 5.3% vs. 22.6% for new mechanisms. 6️⃣ CLINICAL FAILURE CURSE 🚫 A) 150+ ADCs discontinued (e.g., DP303c: 95% keratitis). B) Site-specific tech (DCDS0780A) failed vs. polatuzumab vedotin due to ocular toxicity. 7️⃣ COMBO COMPLEXITY 💣 A) Enfortumab + pembrolizumab doubled OS in bladder cancer… but 46% Grade 3+ toxicities. Food for thought - ADCs are not a guaranteed win. Prioritize companies tackling: 1) Payload innovation (STING agonists, protein degraders). 2)Biomarker-driven trials (HER2 PET, ctDNA). 3)Tumor-selective delivery (pH-sensitive antibodies, peptide masks). Drop a comment: Which ADC player are you betting on? #ADCs #Biotech #Oncology #DrugDevelopment #Investing #Healthcare #investor #startup #pharma

In our lab, we have spent more than a decade developing machine learning methods that move clinical trials from static protocols toward adaptive, continuously learning systems. Our work has spanned four interconnected ML pillars: • Causal effect inference for individualized treatment estimation • Digital twins that generate counterfactual patient trajectories • Adaptive trial design driven by sequential decision learning • AI for pharmacology that connects mechanistic and data-driven reasoning In the process, we developed machine learning that does not merely predict outcomes, but actively reasons about how interventions reshape the future under uncertainty in complex, heterogeneous, and evolving diseases and patient populations.  If you are interested in how ML is reshaping how evidence is generated and how trials can become more intelligent, adaptive, and patient-centered — explore our latest overview below. https://lnkd.in/eRUjP8VG

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

5 RED FLAGS IN CLINICAL TRIAL DESIGN (That every investor spots immediately!) As investor, one can accept scientific risk — it’s part of the game. But not design risk: a trial so flawed in concept or assumptions that even good science never has a chance to shine. Here are five red flags: 1️⃣ ENDPOINTS THAT DON'T MATTER If your primary endpoints are “nice-to-have” biomarkers or surrogate measures that don’t translate into meaningful patient benefit, the trial is at best a research project, not a registrational path. We’ve seen companies chasing endpoints that FDA and EMA would never accept. The result: years lost, capital destroyed, and patients let down. Strong programs align their endpoints early with both regulators and medical practice. 2️⃣ UNDERPOWERED STUDIES This is one of the oldest mistakes in the book. A trial with too few patients, flawed statistical powering, or an overreliance on exploratory subgroups may look promising on a slide deck — but serious investors will see through it instantly. Many Ph2 trials that “suggested” efficacy only to collapse in Ph3, simply because the earlier studies were too small to separate noise from signal. Underpowering is not a sign of efficiency; it’s a sign of cutting corners. 3️⃣ UNREALISTIC RECRUITMENT ASSUMPTIONS Overestimating enrollment speed is one of the most common reasons for trial delays, cost overruns, and credibility erosion. Investors always ask: where will patients come from, how competitive is the indication space, and how realistic are your site projections? 4️⃣ NO RELEVANT COMPARATOR ARM Control arms are the backbone of interpretability. Without them, results are little more than anecdotes. We’ve seen trials lean on historical controls or irrelevant comparators to save time or money. It rarely works. Regulators discount the findings, physicians won’t trust them, and investors won’t back them. A company that can’t commit to proper comparators isn’t seen serious about building approvable data. 5️⃣ IGNORING OR MINIMIZING SAFETY SIGNALS This is perhaps the gravest sin. Early safety signals must be taken seriously. Founders/management teams who brush them aside or explain them away, damage their credibility. History is full of programs that died because safety issues were underestimated or mismanaged. The earlier they are addressed openly and rigorously, the better. Investors notice the honesty with which it is discussed. FOR FOUNDERS: Your trial design tells whether you understand the path to approval and market adoption. If you can explain why your endpoints matter, how your powering is robust, where your patients will come from, and how you’re addressing safety, you’ve already set yourself apart. FOR INVESTORS: The job is to see through excitement and ask the hard questions. Solid trial design is the difference between a “promising story” and an “investable company.” It’s not enough to back good science — one must back good science executed well. #BiotechInvestorInsights

🔬 Towards Decentralized and Privacy-Preserving Clinical Trials 🧠💡Register, learn and build Decentralization in clinical research is not just about scalability or cost-efficiency. It’s a cryptographic transformation that redefines trust and data sovereignty in medical innovation. Technologies like Zero-Knowledge Proofs (ZKPs) and Fully Homomorphic Encryption (FHE) are enabling a new paradigm in decentralized trials: ✅ Privacy without compromising verification: With ZKPs, patients can prove eligibility (inclusion/exclusion criteria) without revealing their full medical history. Compliance is validated without exposing sensitive data. ✅ Computation over encrypted data (FHE): FHE allows researchers to run statistical analyses and predictive models directly on encrypted datasets. No need to decrypt—privacy is preserved even during processing. Ideal for multicenter trials or pharmacogenomic studies. ✅ Traceability without surveillance: Combining blockchain with ZK/FHE enables immutable and auditable recording of clinical events (dosage, adverse effects, outcomes) without identifying the patient. 🌐 In this new model: Data stays where it’s generated (edge computing, patient devices) No centralized data hoarding or exposure risks GDPR and similar regulations are met by design, not workaround 📣 If you're working at the intersection of digital health, cryptography and clinical innovation, this is the future: crypto-technology powering secure, precise, and ethical research. #ZKProofs #FHE #DeSci #DecentralizedTrials #PrivacyByDesign #Web3Health #DigitalTrust #Blockchain #ClinicalResearch #HealthTech Anthony Joaquim José Daniel Dr. Hidenori Vivek Helena Lars Yousuke Carlos Iker Paris João Domingos

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

Top 5 Challenges Faced by Clinical Research Coordinators and How to Overcome Them As a former Clinical Research Coordinator (#CRC) in Basavatarakam Cancer Hospital and Research Institute , I can attest that this role is both rewarding and challenging. CRCs play a crucial role in clinical trials, but they often face numerous obstacles. Let's explore the top 5 challenges and strategies to overcome them. 1. Balancing Patient Care and Administrative Work Challenge: Managing patient work while simultaneously handling EDC (Electronic Data Capture) tasks can be overwhelming. Solution: - Prioritize tasks using time management tools - Allocate specific time blocks for EDC work 2. Managing Multiple Studies and Patients Challenge: Being delegated to multiple studies with numerous patients can lead to information overload and potential oversights. Solution: - Create a master schedule for all studies - Use color-coding systems for different studies - Implement a robust tracking system for patient follow-ups 3. Coordinating with Principal Investigators (PIs) and Patients Challenge: Constantly following up with PIs and patients for lab work, scans, and other study-related activities can be time-consuming and frustrating. Solution: - Use reminder systems (e.g., automated texts or emails) - Build strong relationships to improve cooperation 4. Handling On-Site Monitoring Visits Challenge: On-site monitoring visits add extra pressure, requiring CRCs to work with CRAs while still managing regular duties. Solution: - Prepare for monitoring visits well in advance - Create a monitoring visit checklist - Delegate non-urgent tasks to team members during monitoring days 5. Physical and Mental Fatigue Challenge: The role often involves significant physical work and mental strain, leading to burnout. Solution: - Practice self-care and stress-management techniques - Take regular breaks and use ergonomic equipment Personal Reflection: During my time as a CRC, I often wished for "10 hands and legs" to manage the workload, especially during on-site monitoring. The job required juggling patient care, EDC work, and collaborating with CRAs, all while ensuring the smooth running of multiple studies. Despite the challenges, working as a CRC provides invaluable real-world experience in clinical research. It hones your multitasking abilities, improves your communication skills, and gives you a comprehensive understanding of the clinical trial process. Conclusion: While the role of a CRC is undoubtedly demanding, it's also incredibly fulfilling. By implementing effective strategies and maintaining a positive attitude, CRCs can overcome these challenges and excel in their crucial role in advancing medical research. Have you faced similar challenges as a CRC? What strategies have you found effective? Share your experiences in the comments below! #ClinicalResearch #CRC #ClinicalTrials #HealthcareInnovation #ProfessionalDevelopment

AI could make clinical trials faster, cheaper, and more inclusive, but success depends on explainability, interoperability, and trust. 1️⃣ 80% of trials face recruitment delays, and 50% of datasets contain quality issues; AI aims to fix both. 2️⃣ Machine learning improves protocol design accuracy (80% vs. 65%) and accelerates site selection and feasibility assessments. 3️⃣ AI tools boost enrollment by up to 65% and cut screening time by 78%, though real-world deployment can be costly and complex. 4️⃣ NLP and digital systems help identify underrepresented groups, supporting more diverse and inclusive recruitment. 5️⃣ AI-driven digital biomarkers enable 90% sensitivity in real-time safety monitoring, improving adverse event detection. 6️⃣ Risk-based monitoring powered by AI detects data integrity issues within 48 hours, much faster than manual reviews. 7️⃣ Predictive models achieve 85-90% accuracy in forecasting outcomes and enable adaptive, personalized trial designs. 8️⃣ High-dimensional, noisy, and heterogeneous data challenge AI systems; success requires strong data harmonization and validation. 9️⃣ Regulatory gaps, stakeholder distrust, and lack of explainability remain major barriers to clinical adoption. 🔟 Real-world trials show AI's promise, but also its high cost, customization demands, and integration hurdles. ✍🏻 David Olawade (MPH, FRSPH, FHEA), Sandra Chinaza Fidelis (RN, BNSc, MSc, MPH), Sheila Marinze, Eghosasere Egbon, Ayodele Osunmakinde, Augustus Osborne. Artificial intelligence in clinical trials: A comprehensive review of opportunities, challenges, and future directions. International Journal of Medical Informatics. 2026. DOI: 10.1016/j.ijmedinf.2025.106141

“You people are crooks. How hard can a clinical trial be?” A man said this to me recently after learning I work in clinical research. Honestly? Harder than the neat Phase 1 → Phase 2 → Phase 3 graphic makes it look. From first-in-human dosing to approval can take about a decade. Development can cost billions. And fewer than 1/10 drugs entering clinical trials ultimately reach approval. The obvious question is Why? Every study must: ✔️ protect patients, ✔️ produce credible evidence, ✔️ meet regulatory expectations, ✔️ and function across real sites, real countries, real vendors, and real life. The Ph1 > Ph 3 funnel is useful. It is also real-life naive. Here are a few ordinary places where a neat clinical trial plan meets reality: 👉 Site selection. A site can look perfect on paper, then reveal weak referral pathways, limited capacity, or a competing study chasing the same patients. 👉 Slow start-up. Contracts, budgets, ethics reviews, regulatory submissions, and site activation rarely cross the finish line together. 👉 Recruitment and retention problems. Patients are uninterested or unavailable. Enrolled patients leave the study due to life demands or when faced with a high volume of visits, procedures, travel, side effects, or protocol changes. People and technology change. A trained coordinator leaves. A vendor team changes. A tech integration or data transfer fails at exactly the wrong moment. Study knowledge must be rebuilt while the clock keeps running. 👉 Safety and protocol changes. New information can trigger extra monitoring, difficult decisions, amendments, enrollment pauses, or changes to the study design. 👉 Global and laboratory complexity. One protocol still has to navigate different regulations, import rules, medical practices, shipping routes, sample requirements, and assay performance across countries. And those are only a few notes in a much larger score. Clinical development is not a perfectly rehearsed performance. It is closer to conducting an orchestra while the score, tempo, venue, and a few musicians change mid-performance. Sharp and flat notes are not signs that the work is unnecessary. They are signs that clinical development is a complex system involving patients, science, operations, regulation, technology, and thousands of decisions that must align. The Phase 1 → Phase 2 → Phase 3 funnel is simple. The system behind it is not. ♻️ Repost if you found this useful. And click Elena (Ella) Sinclair + follow + 🔔

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 🦙🦙🦙