AI For Pharma Growth

In this episode of AI For Pharma Growth, Dr Andree Bates speaks with Dr. Jacek Marczyk, co-founder and CEO of BioDynLab, about a contrarian view of computational drug discovery: that the next leap may come not from more data and bigger models, but from physics.
Dr. Marczyk brings a background in aerospace engineering, automotive, Silicon Graphics and complexity science. His work led to quantitative complexity theory, which he now applies to molecules through BioDynLab’s deterministic, training-free approach.
The conversation explores why high precision and high complexity cannot coexist, and why throwing more compute at biological problems does not automatically produce useful knowledge. Dr. Marczyk argues that machine learning can produce impressive outputs, but without explainability, teams may get a result without understanding the physics behind it.
He explains how BioDynLab uses molecular dynamics and complexity theory to study how atoms and amino acids move, how information flows through molecules, and which residues act as key “hotspots” in that dynamic system. Instead of treating molecules as static structures, this approach looks at the motion and information patterns that help determine biological function.
The key message is that AI and physics should not be seen as enemies. In data-sparse areas such as rare diseases, novel targets and first-in-class chemistry, physics-led methods may offer a complementary route to insight, especially where machine learning has little or no training data to rely on.
Topics Covered
Why pharma’s AI gold rush may miss key biology

The principle of incompatibility

Physics-first drug discovery

Quantitative complexity theory

Why explainability matters

Molecular dynamics and information flow

Atomic and amino acid participation factors

Complexity hotspots in molecules

Static structures versus molecular motion

Rare disease and data-sparse discovery

The Pharma AI Enablement Institute is the structure this episode describes.
Foundations everyone starts with, because the regulated reality is common. 
Then tracks that split by function - every function, from discovery and clinical through regulatory, safety, medical affairs, market access, manufacturing and commercial, up to leadership. 
Monthly live office hours with Dr Andree Bates. 
Prompt libraries maintained as the models change. 
Per-person records a functional sponsor can act on and show an auditor.
Hit a problem mid-workflow and your team asks the library in plain language, then lands on the exact video and timestamp where it has already been answered.
One price per business unit, banded by size. No per-seat charges — because per-seat pricing is what causes the failure this episode is about.
See what the curriculum contains for your function →⁠https://eularis.com/institute/⁠ 
Read the long-form argument, including what changed in Article 4 of the EU AI Act in July → ⁠eularis.com/your-ai-training-worked-thats-the-problem-the-ai-capability-problem-pharma-hasnt-named⁠ 
About the Podcast
AI For Pharma Growth is the podcast from Dr Andree Bates, helping pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands and improve company results. The show demystifies AI for biopharma leaders, from start-up biotech through to Big Pharma.
Dr. Andree Bates⁠ LinkedIn⁠ |⁠ Facebook⁠ |⁠ X

In this solo episode of AI For Pharma Growth, Dr Andree Bates explores the AI capability problem pharma has not properly named: training that works in the room, but fails to hold inside the organisation.
Dr Andree explains why one-off workshops, generic AI fluency programmes and broad learning platforms are not enough. They may teach people what AI is, what it can do and where it can fail, but they rarely teach the exact workflows, judgement calls and regulatory context people need for their own roles.
The episode looks at why AI capability fades over time. Some people leave training and build valuable new workflows, while others forget how to apply what they learned within weeks or months. In pharma, that matters because many AI use cases depend on cognitive, accuracy-based judgement: deciding whether a generated summary faithfully represents a source, whether a claim is substantiated, or whether an output can safely enter a regulated workflow.
Dr Andree also explains why generic training can create risk. If usage rises faster than judgement, teams may become more confident with AI without becoming more capable in the workflows where mistakes carry regulatory, compliance or patient safety consequences.
The key message is clear: AI capability needs to be maintained, role-specific and grounded in pharma reality. Training once, or training generically, is not a capability plan.
Topics Covered
Why AI training often fails to hold

The difference between awareness and capability

Why generic AI fluency is not enough

Role-specific AI workflows in pharma

Skill decay and why 90 days matters

Cognitive judgement and regulatory risk

Why confidence can outpace competence

Shadow AI and unmanaged tool use

What real AI capability support must include

The Pharma AI Enablement Institute

The Pharma AI Enablement Institute is the structure this episode describes.
Foundations everyone starts with, because the regulated reality is common. 
Then tracks that split by function - every function, from discovery and clinical through regulatory, safety, medical affairs, market access, manufacturing and commercial, up to leadership. 
Monthly live office hours with Dr Andree Bates. 
Prompt libraries maintained as the models change. 
Per-person records a functional sponsor can act on and show an auditor.
Hit a problem mid-workflow and your team asks the library in plain language, then lands on the exact video and timestamp where it has already been answered.
One price per business unit, banded by size. No per-seat charges — because per-seat pricing is what causes the failure this episode is about.
See what the curriculum contains for your function →https://eularis.com/institute/ 
Read the long-form argument, including what changed in Article 4 of the EU AI Act in July → eularis.com/your-ai-training-worked-thats-the-problem-the-ai-capability-problem-pharma-hasnt-named 

About the Podcast
AI For Pharma Growth is the podcast from Dr Andree Bates, helping pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands and improve company results. The show demystifies AI for biopharma leaders, from start-up biotech through to Big Pharma.

Dr. Andree Bates LinkedIn | Facebook | X

In this episode of AI For Pharma Growth, Dr Andree Bates speaks with Tom Coates, CEO of Presentient, about why interim analysis in clinical trials is ready for a major upgrade.
Interim analyses allow sponsors to look at trial data mid-flight and assess whether a study is likely to succeed or fail, using pre-specified rules. But Tom explains that many phase two and three commercial trials still do not include a pre-planned interim analysis, meaning sponsors often wait far longer than necessary to detect futility or act on early signs of success.
The conversation explores how Presentient is working on next-generation interim analysis and readout strategies, including the BRX platform, which is designed to handle unblinded data while protecting trial integrity. Tom explains why it is not enough to have a powerful algorithm. Sponsors also need secure architecture, audit trails and methods that regulators and data monitoring committees can trust.
Tom also discusses where AI does and does not belong. For high-stakes stop or go decisions, explainability, reproducibility and regulatory confidence matter more than hype. But model-based methods, synthetic data and subgrouping engines may help sponsors better understand which patients benefit, who does not, and how to design trials around more meaningful treatment signals.
The key message is that interim analysis should not be an underused checkpoint. Done well, it can help sponsors stop failing trials earlier, prepare for success sooner and make better decisions with greater confidence.

Topics Covered
Why interim analysis is underused

Stopping trials early for futility or success

Protecting blinding and trial integrity

Secure handling of unblinded data

What data monitoring committees need to see

Where AI fits, and where it does not

Subgrouping and individual treatment effects

Synthetic data and trial simulation

Regulatory confidence and audit trails

The future of continuous trial monitoring

Eularis helps pharma and biotech leaders turn AI activity into board-defensible governed strategy and measurable commercial outcomes.
If your CFO asked tomorrow for the projected return of each major AI initiative - by year, across three years, with explicit adoption, operating cost and redeployment assumptions - could you produce an answer that survives scrutiny?
And if you could: would you know which of those initiatives most moves the company toward the outcomes it's exposed on over the next three years? Those are two different questions, and most organisations can't answer either. A strong initiative-level ROI tells you a project is defensible. It doesn't tell you it belongs among your top five. Capital spent on a second-order opportunity is capital no longer available for a first-order one — and no amount of downstream rigour recovers value that was never strategically prioritised.
The Eularis AI Strategic Blueprint models both levels: a financial case for every prioritised initiative, and a rigorously modelled ranking of which ones create the most material value against your commercial objectives — then sequences them by dependency rather than enthusiasm, with governance designed for pharma's regulatory reality. See what a board-defensible AI strategy contains → eularis.com/ai-strategic-blueprint-for-pharma

About the Podcast
AI For Pharma Growth is the podcast from Dr Andree Bates, helping pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands and improve company results. The show demystifies AI for biopharma leaders, from start-up biotech through to Big Pharma.
Dr. Andree Bates LinkedIn | Facebook | X

In this solo episode of AI For Pharma Growth, Dr Andree Bates explores why many pharma teams do not have an AI problem at all. They have a capability problem.
Dr Andree starts with a simple question: when was your team last properly trained on AI for their specific role? Not when they were given access to tools, licences or a generic use policy, but when they were trained to use AI effectively, safely and compliantly in their actual workflow.
The episode challenges the usual explanations for disappointing AI results: the model was not good enough, the vendor was wrong, the data was not ready, or the organisation resisted change. In many cases, the tools work, the pilots are useful and the training lands. But the working knowledge needed to use AI well is uneven, fragile and decays over time.
Dr Andree explains why this matters so much in pharma. High-value AI work is often judgement-led: medical information responses, payer materials, safety narratives, regulatory documents and MLR-compatible content. AI can support these tasks, but only when users can tell the difference between a strong draft and a merely plausible one.
She also discusses the research behind skill decay, including why cognitive and accuracy-dependent skills fade faster than simple speed-based or physical skills. That is especially important in pharma, where the cost of a confident but wrong output can become a compliance, regulatory or patient safety issue.
The key message is clear: AI capability is not something you achieve once. It has to be maintained. The functions that lead in AI will not simply be the ones with the most licences or training events. They will be the ones that treat capability as something with a rate of decay and build systems to keep it current.
Topics Covered
Why AI underperformance is often a capability problem

The difference between access, policy and real training

Why confident AI use varies across teams

AI in judgement-led pharma workflows

Skill decay and why 90 days matters

Why high-value AI workflows are often forgotten fastest

The risk of outdated working knowledge

Why training is ignition, not maintenance

The limits of AI champions and internal portals

Three questions to ask your function this week

Eularis helps pharma and biotech leaders turn AI activity into board-defensible governed strategy and measurable commercial outcomes.
If your CFO asked tomorrow for the projected return of each major AI initiative - by year, across three years, with explicit adoption, operating cost and redeployment assumptions - could you produce an answer that survives scrutiny?
And if you could: would you know which of those initiatives most moves the company toward the outcomes it's exposed on over the next three years? Those are two different questions, and most organisations can't answer either. A strong initiative-level ROI tells you a project is defensible. It doesn't tell you it belongs among your top five. Capital spent on a second-order opportunity is capital no longer available for a first-order one — and no amount of downstream rigour recovers value that was never strategically prioritised.
The Eularis AI Strategic Blueprint models both levels: a financial case for every prioritised initiative, and a rigorously modelled ranking of which ones create the most material value against your commercial objectives — then sequences them by dependency rather than enthusiasm, with governance designed for pharma's regulatory reality. See what a board-defensible AI strategy contains → eularis.com/ai-strategic-blueprint-for-pharma
About the Podcast
AI For Pharma Growth is the podcast from Dr Andree Bates, helping pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands and improve company results. The show demystifies AI for biopharma leaders, from start-up biotech through to Big Pharma.

Dr. Andree Bates LinkedIn | Facebook | X

In this episode of AI For Pharma Growth, Dr Andree Bates speaks with Nijat Ahmadov, CEO of Nucs AI, about molecular imaging as one of pharma’s most underused data assets.
Nijat explains why PET, CT and other molecular imaging data remain largely “untouched”: clinically valuable and created at scale, but still too often trapped in qualitative reads rather than structured, standardised data that can support decision making. As radioligand therapies expand in oncology, that gap becomes harder to ignore.
The conversation explores how AI can help turn molecular imaging into computable, decision-grade data for patient selection, response monitoring and companion diagnostic strategy. Nijat argues that AI is no longer a nice-to-have in this space. Without it, pharma risks losing confidence in the outcomes that affect adoption, reimbursement and commercial success.
They also discuss what it will take for AI-derived imaging biomarkers to become regulatory grade: analytical validation, reproducibility, diverse data sets, clinical validation and evidence that endpoints are meaningful, not just technically impressive.
The key message is that imaging is not only diagnostic. Once structured properly, it can reveal predictive signals about disease behaviour and treatment response, making it a powerful asset for pharma teams building the next generation of oncology trials.
Topics Covered
Why molecular imaging is still underused

Turning PET and CT scans into structured data

Radioligand therapy and patient selection

Moving beyond eligible vs not eligible

AI-derived imaging biomarkers

Clinical validation and regulatory trust

Imaging data as a competitive moat

Why prediction matters more than diagnosis

Eularis helps pharma and biotech leaders turn AI activity into board-defensible governed strategy and measurable commercial outcomes.
If your CFO asked tomorrow for the projected return of each major AI initiative - by year, across three years, with explicit adoption, operating cost and redeployment assumptions - could you produce an answer that survives scrutiny?
And if you could: would you know which of those initiatives most moves the company toward the outcomes it's exposed on over the next three years? Those are two different questions, and most organisations can't answer either. A strong initiative-level ROI tells you a project is defensible. It doesn't tell you it belongs among your top five. Capital spent on a second-order opportunity is capital no longer available for a first-order one — and no amount of downstream rigour recovers value that was never strategically prioritised.
The Eularis AI Strategic Blueprint models both levels: a financial case for every prioritised initiative, and a rigorously modelled ranking of which ones create the most material value against your commercial objectives — then sequences them by dependency rather than enthusiasm, with governance designed for pharma's regulatory reality. See what a board-defensible AI strategy contains → eularis.com/ai-strategic-blueprint-for-pharma
About the Podcast
AI For Pharma Growth is the podcast from Dr Andree Bates, helping pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands and improve company results. The show demystifies AI for biopharma leaders, from start-up biotech through to Big Pharma.

Dr. Andree Bates LinkedIn | Facebook | X