AI For Pharma Growth

Some of the most powerful breakthroughs happen when methods built for one discipline get turned on another. In this episode, Dr Andree Bates interviews Dr Irina Babina, CEO of Concr, on how computational techniques originally developed in astrophysics are being applied to oncology, helping predict how individual cancer patients will respond to treatment.Irina shares her journey from genetics and targeted cancer therapy into building applied solutions, driven by a frustration many scientists recognise: good science doesn’t always reach patients fast enough. A pivotal patient experience reinforced her focus on personalised biology, because behind every dataset is a person, and oncology cannot be solved purely through averages.Concr’s approach is built around Bayesian computation and uncertainty-aware modelling. Instead of assuming clean, complete datasets, the system is designed to work with missingness and fragmentation, updating predictions as new evidence comes in. Irina explains how Concr connects mechanistic biological modelling and preclinical drug perturbation data to patient multi-omics, imaging, treatment response, and outcomes data from both clinical trials and real-world settings.A key application is Concr’s patient-level digital twin (“Farsight Twin”), which simulates an individual’s probability of response across therapies, estimates likely benefit, and helps stratify patients earlier in development. Irina shares a use case where Concr supported indication ranking from cell line data, then helped interpret phase 1 signal by estimating which patients benefited from the novel drug versus standard of care, enabling sharper inclusion and expansion planning.Looking ahead, Irina argues we’re moving toward personalised oncology where population-level protocols fade, and decision-making becomes confidence-based, adaptive, and informed by longitudinal monitoring as tumours evolve over time.Topics CoveredApplying astrophysics-inspired methods to cancer biologyBayesian computation and modelling uncertaintyIntegrating multi-omics, imaging, trials, and real-world evidenceTranslational modelling from preclinical to clinical outcomesPatient-level digital twins and therapy response simulationStratification, enrichment, and reducing early-stage uncertaintyPan-cancer modelling to improve rare cancer predictionThe future of personalised oncology and dynamic monitoringEularis helps pharma and biotech leaders turn AI activity into board-defensible strategy and measurable commercial outcomes.If your organisation has plenty of AI in motion but very little that moves the commercial needle in a way the board can see, start with our 10-Day AI Diagnostic Sprint. It’s a focused diagnostic that surfaces what’s actually broken and what’s blocking results, before you invest in a larger strategy effort.The Sprint diagnoses the problem. The AI Strategic Blueprint that follows is where we build the board-defensible strategy and plan. Details at eularis.com.If this episode described your situation, send me a LinkedIn DM starting with ‘SENSECHECK’ and two things: the question you’re trying to answer internally, and what’s currently in flight. I’ll reply with what I’d need to see to turn that activity into a defensible plan, and the next step.About the PodcastAI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.Dr. Andree Bates LinkedIn | Facebook | X

Pharma doesn’t have an AI experimentation problem. It has an AI execution, scaling, and ROI justification problem. In this solo episode, Dr Andree Bates names one of the most expensive failure patterns in the industry: pilot purgatory.A key theme is misdiagnosis. When AI stalls, organisations often blame platforms, data science capability, training, vendor selection, or “resistance to change”. Dr Andree argues these explanations are usually incomplete because they ignore the structural constraints that determine whether AI gets trusted, governed, adopted, and tied to real decisions at scale.She outlines the core blockers she sees repeatedly: governance ambiguity, unresolved decision rights between global and local teams, data ownership disputes, incentive misalignment across functions, and adoption friction caused by tools that were never designed around real workflows. Treating adoption as a comms issue or solving with yet another pilot simply keeps the constraint untouched.Finally, Dr Andree explains what breaking out of pilot purgatory actually takes: clear executive ownership of business outcomes (not just technical delivery), defined decision points where AI changes action, governance that accelerates scale rather than blocking it, cross functional stewardship models, and defensible value logic that survives board scrutiny. The organisations pulling ahead aren’t running the most pilots. They’re confronting what’s structurally broken early, then building a strategy and sequencing plan that makes scale inevitable.Topics CoveredWhat “pilot purgatory” is and why it’s so costlyThe tell tale signs: pilots, duplication, uneven adoption, decorative AIWhy more pilots can make the stall worseCommon misdiagnoses: tools, training, vendors, “resistance”The real blockers: governance, decision rights, data ownership, incentivesWhy adoption is a downstream symptom, not the core problemRebuilding defensible ROI logic and board ready financial modelsWhat “good” looks like when organisations break out of purgatoryWhy delay compounds scepticism and weakens transformation capacityEularis helps pharma and biotech leaders turn AI activity into board-defensible strategy and measurable commercial outcomes.If your organisation has plenty of AI in motion but very little that moves the commercial needle in a way the board can see, start with our 10-Day AI Diagnostic Sprint. It’s a focused diagnostic that surfaces what’s actually broken and what’s blocking results, before you invest in a larger strategy effort.The Sprint diagnoses the problem. The AI Strategic Blueprint that follows is where we build the board-defensible strategy and plan.Details at eularis.com.If this episode described your situation, send me a LinkedIn DM starting with ‘SENSECHECK’ and two things: the question you’re trying to answer internally, and what’s currently in flight. I’ll reply with what I’d need to see to turn that activity into a defensible plan, and the next step. About the PodcastAI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how the use of AI-based technologies can save them time and grow their brands and improve company results.This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.If you want, I can also pull 3 “quote graphic” alternates that are shorter and sharper from this solo transcript (there are loads of killer lines in here).Dr. Andree Bates LinkedIn | Facebook | X

Cell therapies have huge potential, but cost, complexity, and centralised manufacturing have kept many of them confined to last-line use. In this episode, Dr Andree Bates speaks with Armon Sharei, Founder and CEO of Portal Biotechnologies, about what happens when AI meets cell engineering, and why point-of-care delivery could make personalised cell programming more practical, scalable, and safer.Armon explains Portal’s core idea: cells are programmable machines. If you can reliably deliver multiple cargoes into cells, you can instruct new behaviours. Portal’s method briefly “squishes” cells through precision pores to disrupt the membrane so external material can enter, opening the door to complex cell engineering without permanent genome edits.They explore where AI fits in: modelling cell behaviour. By combining perturbation experiments, rich readouts, and phenotypic screening, AI can help generate “virtual cell models” that suggest which RNA instructions to deliver to drive specific outcomes. The bottleneck is data at the right complexity, because many effects only appear when multiple pathways are changed at once.A key takeaway is the safety and flexibility of transient RNA reprogramming. Unlike irreversible genetic modification, RNA fades within days, reducing long-term risk and making earlier-line use more realistic. Armon also discusses how point-of-care workflows may be regulated differently, with the machine treated as a device and the cargo as the drug.Looking ahead, he paints a vision of infusion-centre cell programming: a compact system that takes blood, delivers tailored RNA instructions to immune cells, and returns them within hours, potentially bringing costs closer to mainstream biologics and expanding access.Topics CoveredWhy delivery is the unlock for programmable cell therapiesHow Portal’s “cell squishing” delivery worksUsing AI to model cells and generate functional programmesThe data bottleneck and why multi-perturbation datasets matterPhenotypic screening and lab-to-clinic feedback loopsTransient RNA reprogramming vs permanent genetic modificationRegulatory implications of point-of-care engineeringEconomics and scalability of point-of-care approachesNear-term opportunities in oncology and autoimmunityThe future vision for infusion-centre personalised therapiesEularis helps pharma and biotech leaders turn AI activity into board-defensible strategy and measurable commercial outcomes.If your organisation has plenty of AI in motion but very little that moves the commercial needle in a way the board can see, start with our 10-Day AI Diagnostic Sprint. It’s a focused diagnostic that surfaces what’s actually broken and what’s blocking results, before you invest in a larger strategy effort.The Sprint diagnoses the problem. The AI Strategic Blueprint that follows is where we build the board-defensible strategy and plan.Details at eularis.com.About the PodcastAI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.AI platforms and tools solve specific problems. Strategy makes sure you’re solving the right ones, in the right order. If you want help mapping priorities as you evaluate what to roll out next, send me a LinkedIn DM starting with ‘PRIORITIES’ and two lines: what’s already in flight, and the decision you’re trying to make next.Dr. Andree Bates LinkedIn | Facebook | X

Clinical trial site selection is one of the biggest hidden bottlenecks in drug development, and it’s still often driven by legacy relationships, spreadsheets, and habit. In this episode, Dr Andree Bates interviews Simon Arkell, founder of Ryght, Inc, about “AI Site Twins” and why the next era of site selection shifts from institutional memory to predictive, real-time analytics.Simon explains why the current model produces terrible outcomes at scale: too many activated sites under-enrol, competition at sites is poorly understood, and sponsors often don’t see the failure until timelines have already slipped. He argues this is primarily a site selection problem, because “the easy button” of re-using familiar sites reduces data-driven decision making, even as trials get more complex and patient competition intensifies.Ryght’s approach is to build AI-powered digital replicas of research sites, creating a unique identifier and a dynamic “twin” profile that continuously improves as new data arrives. Simon walks through how protocols can be matched to sites across countries, then enriched using harmonised public data, competitive trial context, and automated outreach that dramatically increases engagement. He also describes how different AI agents help fill missing information, find the right contacts, and capture context across email, portals, and voice interactions to improve future matching.The upside is massive: faster feasibility, better site choices, shorter time-to-activation, earlier first-patient-in, and ultimately faster time-to-market. Simon links these operational gains to commercial reality: every month saved can mean earlier revenue, longer effective patent runway, and more lives impacted by getting therapies to patients sooner.Topics CoveredWhy site selection is still a major bottleneck in clinical trialsThe true cost of underperforming sites and enrolment failureWhat an AI Site Twin is and how it differs from legacy databasesGlobal protocol-to-site matching and competitive trial contextData harmonisation from messy public sourcesAgent workflows: enrichment, outreach, contact finding, and context captureEngagement rates and accelerating feasibility timelinesEnrolment curve modelling and predicting site performanceSecurity, HIPAA/GDPR compliance, and sponsor data integrationTime-to-activation, first-patient-in, and time-to-last-patient-in KPIsWhy “execution speed” and flywheels create a moat in AI applicationsEularis helps pharma and biotech leaders turn AI activity into board-defensible strategy and measurable commercial outcomes.If your organisation has plenty of AI in motion but very little that moves the commercial needle in a way the board can see, start with our 10-Day AI Diagnostic Sprint. It’s a focused diagnostic that surfaces what’s actually broken and what’s blocking results, before you invest in a larger strategy effort.The Sprint diagnoses the problem. The AI Strategic Blueprint that follows is where we build the board-defensible strategy and plan.Details at eularis.com.About the PodcastAI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.

For many life sciences teams, the first wave of AI has looked like copilots: smart search, quick answers, and help on demand. Useful, but passive. In this episode, Dr Andree Bates is joined by Parth Khanna, CEO and co-founder of ACTO, to explore what comes next: moving beyond copilots into role-based AI agents that proactively close knowledge gaps, improve field readiness, and operate safely inside regulated environments.Parth shares his path into life sciences and tech, including founding an early NLP company in 2012 and then building ACTO after speaking with over 100 life science companies about field force effectiveness. Today, ACTO supports tens of thousands of professionals and hundreds of brand launches, and Parth argues the industry is now entering the “agentic era” where the real differentiator is not just model access, but how organisations build context, control, and change management around AI.A key theme is why generic AI tools often fail inside enterprises. Parth outlines four requirements for agent success: context (role and job-specific personalisation), connection (stitching data sources and agent-to-agent workflows), control (testing, monitoring, observability), and change management (reducing fear and driving adoption). Without these, he says, many copilots and assistants end up underused, with people quietly reverting to old workflows.Parth then introduces ACTO’s concept of role-based “super agents”, designed around a real job description (for example an MSL). Rather than a disconnected swarm of task bots, a “queen bee” orchestrator agent delegates to worker agents, checks outputs against compliance guardrails, and can be assessed with exams to quantify risk before deployment. This approach, he argues, makes AI both more powerful and safer for regulated field teams.Finally, the conversation looks ahead. Parth believes the future of work depends on pairing AI capability with distinctly human strengths: strategy, judgement, and human connection. The winners won’t be those who automate the most tasks, but those who redesign roles so humans and agents amplify each other.Topics CoveredWhy copilots are useful but fundamentally passiveThe shift from AI that responds to AI that actsWhy generic tools fail: context, connection, control, change managementAdoption reality: why many AI assistants go unusedQuantifying risk and moving from black box to observable AIRole-based super agents and the “queen bee” orchestrator modelTesting agents with exams before field deploymentGuardrails, compliance, and agent-to-agent quality checksHuman skills AI can’t replace: strategy, judgement, connectionThe future of MSL and field excellence in an agentic eraEularis helps pharma and biotech leaders turn AI activity into board-defensible strategy and measurable commercial outcomes.If your organisation has plenty of AI in motion but very little that moves the commercial needle in a way the board can see, start with our 10-Day AI Diagnostic Sprint. It’s a focused diagnostic that surfaces what’s actually broken and what’s blocking results, before you invest in a larger strategy effort.The Sprint diagnoses the problem. The AI Strategic Blueprint that follows is where we build the board-defensible strategy and plan.Details at eularis.com.About the PodcastAI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.