Animal-free drug discovery is closer with QSP

Quantitative Systems Pharmacology (QSP) is rapidly moving from niche innovation to a standard tool in drug development. In the hands of researchers, QSP can simulate complex drug–disease interactions, predict human responses before clinical trials and guide more ethical, efficient research strategies.

To explore where the field stands today – and where it is heading – Drug Target Review spoke with Dr Josh Apgar, Vice President and Head of QSP Software at Certara. Apgar’s career spans pharmaceutical R&D, mechanistic modelling and software development, giving him practical insight into how QSP can be applied to improve discovery processes and support the transition towards non-animal testing.

From pharma bench to software leadership

Apgar has worked in QSP from both the pharmaceutical and technology developer perspectives. His career includes roles within a pharmaceutical company, as a modelling service provider and now as a technology lead. These experiences have shaped his goal of creating the next generation of technology.

“My role is to develop the next generation of tools and technology to help QSP reach its full potential.”

Apgar believes that while QSP’s promise is well recognised, its full impact has been limited by the maturity and scale of current technology. That, he says, is starting to change.

QSP in early discovery

In Apgar’s view, QSP is already a critical element in the early pipeline.

“QSP is no longer just an emerging tool, it is becoming a standard in drug development.”

By combining computational modelling with experimental data, QSP can replicate biological processes and simulate how interventions might behave in virtual patients. The result is actionable insight before a molecule ever reaches a human subject.

At Certara, these models are routinely used to assess target feasibility, identify optimal dosing strategies and predict clinical outcomes based on preclinical data. “These insights can shape the design of future experiments, reduce the need for animal testing and better position candidates for success later in development,” Apgar says.

A central player in New Approach Methodologies

With global momentum building for New Approach Methodologies (NAMs), QSP’s role as a human-relevant alternative to animal testing is increasingly in the spotlight.

“QSP plays a vital role in New Approach Methodologies by offering a human-relevant, mechanistic alternative to traditional animal testing.”

Unlike animal models, QSP simulations can account for species-specific biology, predict human exposure and explore potential immune responses. Mechanistic modelling, Apgar notes, can also integrate diverse datasets – from in vitro results to patient variability – making it possible to anticipate efficacy and safety with far greater translational accuracy.

He adds that in certain contexts, QSP can “reduce or even replace the need for animal studies”, advancing both ethical imperatives and scientific precision.

New Approach Methodologies (NAMs) use human-relevant tools such as in silico modelling, in vitro assays and computational simulations. They aim to reduce or replace animal testing while improving accuracy in drug discovery. Image courtesy of Shutterstock/metamorworks.

The data behind the models

Robust QSP models depend on a blend of biological and pharmacological data. At Certara, Apgar’s team draws from in vitro and in vivo pharmacokinetics/pharmacodynamics (PK/PD), disease progression metrics and biomarker data linked to a therapy’s mechanism of action.

“Most importantly,” he says, “they can increase the amount of clinical data by bridging studies with comparator molecules, rather than relying on imprecise animal analogs.”

The aim is to build models that mirror human disease and therapeutic response closely enough to guide confident, evidence-based decisions – even in the absence of animal testing.

Regulatory bodies are paying attention

Regulatory processes have been slow to adapt to new modelling approaches, but Apgar says this is changing.

“Regulatory bodies are increasingly recognising the value of QSP and other modelling approaches as part of a modernised, science-based evaluation process.”

Certara has already supported numerous successful submissions incorporating QSP models, often alongside physiologically based pharmacokinetic (PBPK) modelling and in vitro to in vivo extrapolation (IVIVE).

Regulators, however, are looking for models that are well validated, transparent and closely aligned to the questions at hand. “We help clients prepare regulatory-aligned data packages, participate in pre-submission meetings and engage with agencies like the FDA to demonstrate how QSP can reduce reliance on animal studies and improve human relevance,” Apgar explains.

Where AI meets mechanistic modelling

The integration of artificial intelligence into QSP workflows is still in its early stages – but growing fast. Machine learning, Apgar says, is already helping to process complex datasets, inform parameter values and accelerate model calibration.

“AI is already making QSP more scalable and efficient by reducing manual steps and accelerating iteration cycles.”

Apgar says AI could help identify biological patterns, automate modelling tasks and improve sensitivity analysis, making QSP easier and faster to use.

The hub of a connected biosimulation future

Apgar describes QSP as a central link between biosimulation strategies. In his view, it can connect methods ranging from atomic-scale modelling to large-scale machine learning with real-world evidence, enabling combinations that no single approach could achieve.

The end-goal? A human-focused, collaborative model of drug development where animal testing is no longer the default.

Ethical, efficient and evidence-driven

As public and policy pressure mounts to replace animal testing where possible, QSP offers a scientifically rigorous path forward. By integrating biology, pharmacology and clinical context into a single model, it supports ethical research strategies without compromising – and often improving – translational accuracy.

For Apgar, the next phase is about scale and accessibility. The challenge is not proving QSP’s worth, but building the tools, workflows and regulatory acceptance needed to embed it fully into the industry’s decision-making fabric.

“QSP will continue to support this transition,” he says, “by providing scientifically rigorous, human-relevant alternatives.”

The take-home message

From Apgar’s perspective, QSP has crossed a threshold: no longer experimental, it is becoming an essential part of early-stage discovery. Its ability to model human-specific responses, integrate diverse datasets and operate alongside AI and other biosimulation methods positions it as a central driver of both innovation and ethical progress.

If the industry continues to invest in QSP’s scale and integration, the traditional animal-centric paradigm of preclinical research could give way to a more predictive, humane and connected future – one in which researchers can explore new drugs with confidence long before the first patient dose.