2026 PharmDev Roadmap: The Predictive Model

In the first two installments of this series, we explored 2 major reasons why pharmaceutical development and manufacturing (PharmDev) is being forced to change: supply chains are fragmenting (Part 1) and pipelines are becoming more complex due to the rise of personalized medicine (Part 2).

The question we’re left with is how operating models will adjust in response. What is emerging across the industry is a move toward more predictive, data-driven decision-making, not because our teams suddenly lack data, but because learning too late has become increasingly expensive.

The Predictive Model – Enabling AI-Driven Process Control

PharmDev has always been iterative: we run experiments, learn from what fails, and refine the process as we go. In 2026, this approach is under pressure as low-volume programs, compressed timelines, and limited tolerance for rework make iteration a default that is no longer sustainable.

At the same time, development teams generate more experimental and analytical data than ever before. Historically, much of this data has been evaluated in isolation. Individual studies answer local questions, but the learning does not always carry forward. As modeling approaches are applied more broadly, datasets start to accumulate in a meaningful way: insights from earlier experiments can inform later decisions, instead of being rediscovered after the fact.

This moves development away from correcting problems after rework and toward anticipating failure before losing time, material, and cost.

Small Molecules: Reducing Late-Stage Churn

In small molecule development, programs rarely fail because of a single issue. Instead, problems build over time: a formulation that looks acceptable early on requires repeated adjustment, impurity profiles evolve in unexpected ways, stability data raises questions late in development, or control strategies that seemed reasonable at small scale become difficult to defend under regulatory scrutiny. When these issues surface late, options are typically extremely limited and changes are both costly and slow to implement.

Predictive approaches help reduce this kind of churn. By integrating formulation screening data, process development results, and impurity and degradation studies, models can be used to explore how changes in route, composition, or processing conditions are likely to affect critical quality attributes (CQAs). This is particularly useful for impurity trend analysis and stability modeling, where early route-scouting, forced degradation, and accelerated stability data can inform control strategies before extensive physical iteration is required.

Early examples of this approach are already appearing in practice. Researchers at Merck KGaA have described AI-based predictive formulation tools used to evaluate co-former combinations and identify soluble, stable formulations more efficiently. The value here comes in the form of time saved by avoiding prolonged trial and error during formulation development.

Similar model-informed approaches are increasingly being applied at top CROs, CMOs, and CDMOs, where predictive assessment of route, impurity risk, and manufacturability is being used to converge on viable control strategies earlier in development.

Biologics: Preventing Irreversible Loss

In biologics development, the role of prediction looks different since the challenge is often not optimization, but fragility. Variability introduced early can propagate through the process in ways that are difficult (and in some cases, impossible) to correct later.

Models built on cell culture data, process characterization studies, and analytical results are increasingly used to understand sensitivity. This allows teams to answer questions such as:

• Which parameters have the greatest influence on CQAs?
• Where are operating margins narrow?
• How is variability moving through the process?

Leading biologics sponsors and top CDMOs alike have increasingly emphasized model-informed process development to understand sensitivity, define operating margins, and identify manufacturability risk earlier. Identifying high-risk parameters earlier allows these teams to:

• detect process risk before batch failure occurs,
• apply adaptive control strategies as processes evolve,
• reduce failed or borderline runs during development and scale-up.

From Development into GMP Manufacturing

While predictive models are often discussed in the context of development, their impact becomes even more consequential once processes transition into GMP manufacturing. In a GMP environment, uncertainty no longer results in additional experiments or extended development timelines. It shows up as deviations, investigations, constrained operating ranges, batch failures, or supply disruption.

Predictive models that carry forward development knowledge into manufacturing enable clearer definition of operating ranges, control strategies, and expected sources of variability before GMP execution begins. Rather than relying on reactive investigation after excursions occur, manufacturing teams can anticipate where risk is most likely to emerge and design controls accordingly.

In this way, predictive development directly supports more stable GMP manufacturing. Learning is no longer confined to early-stage studies, but becomes embedded in how processes are executed, monitored, and adjusted over time. The benefit is not simply operational efficiency, but reduced manufacturing risk and greater confidence as programs scale and supply commitments increase.

How Prediction Changes Sponsor and Supplier Alignment

In Part 2, we discussed why PharmDev has moved away from single-vendor models and toward networks of specialized partners. As predictive models move key decisions earlier and reduce uncertainty, they also reshape how sponsors and suppliers align around those decisions across development.

Predictive development compresses decision timelines and moves key inflection points upstream. That places new demands on how sponsors and suppliers interact. Alignment can no longer be based solely on scope definition, capacity commitments, or clean handoffs between stages. It increasingly depends on shared technical understanding and the ability to act on evolving data in real time.

This alignment often begins at the vendor discovery and engagement stage. As predictive models move decisions upstream, sponsors are looking for potential partners that are not only technically capable, but prepared to operate within GMP expectations once programs transition into manufacturing. That includes verified quality systems, documented GMP experience, and the ability to support structured qualification from the start.

R&D orchestration platforms like Scientist.com support this shift by making GMP and technical readiness more transparent through RFIs and pre-assessments, allowing sponsors and suppliers to align before development paths and timelines are locked.

As predictive insight becomes more central to PharmDev, success depends less on who controls the most capacity and more on whether sponsors and suppliers can align on how decisions are generated, evaluated, and acted upon. In practice, this shift is forcing a rethink of what partnership actually means in a capacity-constrained world.

Next in the series: The Strategic Partner: Rethinking the CDMO Relationship in a Capacity-Constrained World