By Paul Weiss, PhD, Partner at Venture Investors Health Fund

In breast cancer, there was a moment when outcomes changed dramatically, but only for certain patients.

Herceptin, an antibody therapy, was developed to target an overexpression of the HER2 protein. If a patient was HER2-positive, Herceptin worked. If they were not HER2-positive, it didn’t.

That was one of the clearest early demonstrations of precision in therapeutics. You look at the biomarker. If it’s present, you treat. If it’s not, you don’t.

It wasn’t just about having a drug: it was about knowing which patients would benefit from it. That, for me, is what precision in treatment really means.

Precision Beyond Discovery

Therapeutics are often framed as the end point of precision medicine, typically as a discovery concept. You find a mutation in a pathway and develop a therapy around it.

But in treatment, precision means clinical specificity. It’s about knowing:

• Which patients to treat
• Which patients will benefit
• What stage of disease they are in
• Whether you can monitor response
• And whether precision can guide combination therapies

This is a fundamental theme across a number of our companies at Venture Investors Health Fund.

It’s not just about the molecule. It’s about integrating diagnostics into the care pathway so the right patients are identified and unnecessary exposure in both cost and side effects is avoided.

Matching Therapy to Biology

There are multiple examples where matching therapy to biology materially changed outcomes.

In lung cancer, once EGFR mutations were identified and tyrosine kinase inhibitors (TKIs) were developed to target them, survival improved dramatically. You assess the patient’s EGFR mutational status. If present, you treat with a TKI. If not, you don’t.

Second-generation TKIs were later developed as resistance emerged—another layer of precision.

In cystic fibrosis, CFTR modulators were matched to patients through genotyping, significantly changing outcomes for certain patients.

And in immuno-oncology, drugs like PD-1 inhibitors are often prescribed only when patients are PD-1 positive. Even there, we are refining precision, but not all PD-1 positive patients respond.

The FDA has long encouraged development of companion diagnostics when biomarkers exist. It’s common in oncology and rare diseases. Precision is increasingly embedded into development strategy.

One of our portfolio companies, Iterion Therapeutics, enriches its patient population by targeting beta-catenin mutations. That increases the likelihood of clinical success and avoids exposing patients to therapies unlikely to work.

Another, Elephas, has developed a platform to test tumor biopsy samples and determine whether a patient will respond to immuno-oncology therapy. Their observational trials have shown strong correlation between their predictions and patient outcomes. When fully commercialized, this becomes a clinical decision tool, helping determine who should receive IO therapy and who should not.

Precision improves outcomes, and it also improves economics.

How AI Is Reshaping Development

AI is a fast-changing field, and we’re still in the early stages. Where it’s already helping is in target identification and validation, reducing biological risk by moving through enormous datasets more quickly. We understand animal models and organoids well. As the saying goes, we’ve cured a lot of mice—but translating that success into human outcomes has always been difficult.

AI has the potential to make that translational step more predictable. As development progresses, AI can optimize clinical trial design. Through biomarker strategies and what I call clinical trial enrichment, companies can build patient selection directly into inclusion criteria, increasing the likelihood that a drug demonstrates benefit.

AI may also help derive smarter endpoints and mine massive real-world datasets, including electronic medical record systems, to identify patient populations where a therapy may be more applicable. The goal is a more efficient pathway from drug discovery to approval.

What Innovation in Therapeutics Really Looks Like Today

When I started in biotechnology, innovation was centered on the molecule itself: novel targets, novel chemistry. Today, innovation is just as much about proving that the right patients will show therapeutic benefit.

Especially in oncology, we’re seeing increased use of combination therapies and new modalities like bispecific antibodies that approach two different targets.

But innovation isn’t just the drug. It’s matching that modality to specific patient populations.

For us, this also ties to capital efficiency. We want the most logical and capital-efficient path to clinical proof of concept. That often involves a biomarker-driven precision strategy.

Evaluating Precision Beyond the Molecule

When we evaluate opportunities, we look for precision across the entire development pathway. Do they have:

• A clear biomarker strategy?
• Built-in patient identification?
• Diagnostic alignment?
• Regulatory positioning?
• A coherent partnering strategy?

It’s not just about having a potent molecule against a target. Precision spans regulatory, commercial, and diagnostic alignment.

It used to be that you started with interesting science and then found a use for it. Today, we think about reimbursement strategy and unmet medical need from the outset.

Real-world data also plays an increasing role. These enormous datasets can inform patient selection and development strategy if accessed appropriately.

From Targeted Drugs to Adaptive Systems

Over the next decade, therapeutics will become increasingly programmable within large data ecosystems. Diagnostics won’t just select patients at baseline. They’ll monitor therapy.

For example, sleep scoring data from EnsoData can establish baseline measurements and monitor whether a narcolepsy therapy is working. EarliPoint can diagnose autism and monitor treatment progress over time.

In oncology, circulating tumor DNA can be monitored to determine whether a therapy is effective. Classic biomarkers like PSA, LDL cholesterol, A1C, and blood pressure have long served this purpose.

Going forward, more advanced diagnostics will integrate directly with therapy monitoring, particularly in combination therapies.

Defining Precision Medicine 2.0

Precision Medicine 2.0 means continuously adapting treatment to a patient’s evolving biology, or the evolution of their disease, using real-time data. It’s not just matching a drug to a mutation once. It’s an ongoing process.

We are moving from discovering targeted drugs to building integrated systems that link therapy and real-time data, and now the challenge is scale. Can we design therapies and diagnostics that deliver higher confidence that treatments will work, monitor them effectively, and make that precision accessible to large patient populations? That is the next phase of therapeutics.

It’s not only about better drugs, it’s about building systems that ensure those drugs reach the right patients, and continue to adapt as biology evolves.

Stay tuned for more Precision Medicine insights from the VI team.