Is the end of cancer conceivable in the future? Is it possible to eradicate this disease from our bodies and our societies forever? The answers to these questions are embedded in the biology of this incredible disease.
These are Siddhartha Mukherjee’s words, from his extraordinary book ‘The Emperor of all Maladies’. They highlight the importance of untangling the biological processes that drive cancer if we are to find a cure.
But how do we connect those biological laws with the symptoms we observe in patients?
How to design a therapy that navigates those laws successfully? Out of all the quantitative sciences at our disposal to help us reduce uncertainty in this quest, QSP (Quantitative Systems Pharmacology) is the only approach that makes an explicit connection between drug, biology, and disease.
QSP is a reminder that drug therapies are a chemical intervention. Through QSP, we describe the dynamics of the chain reaction that follows downstream from the interaction between the drug and its target, all the way to changes in biomarker levels and clinical endpoints.
The initial medical practices were guided by the changes that doctors saw in their patients’ organs. Organs were the minimal functional units, because they were tangible to the naked eye. With the advent of new technologies, physicians can move from a macroscale to a microscale, examining cell levels (using microscopes and dyes) and then protein, RNA, and DNA levels (using -omics). QSP is often described as a ‘multiscale’ approach because it can be stretched to capture that continuum.
QSP is a reminder that drug therapies are a chemical intervention.
As a general assumption, for QSP, cells are the minimal functional unit in the human body. We can observe changes in cell functions via biomarkers. Whether a human is healthy or sick depends on how well interconnected, balanced, and synchronized the functions of all the human body cells are. We diagnose and monitor disease via biomarker changes in patients. Drug targets modulate cell function, and in turn, drugs modulate those targets.
The ability of QSP to deal with the dynamics of such a hierarchical system can mean different things to different kinds of drug developers. For bioinformaticians aiming to identify new molecular targets or novel combinations, and for molecular biologists seeking to validate them, QSP can be equivalent to a loss-of-function analysis or an in silico CRISPR experiment.
At the other end of the drug development process, for clinical scientists, QSP can serve as a means to enrich patient data with data derived from biological mechanistic knowledge. For principal investigators of clinical trials, QSP can give the key to identifying responders from non-responders, because it captures the biological diversity within our species.
To summarize, in the right hands, at the right moment, QSP can become a true enabler for Siddhartha Mukherjee’s plea – when the integration of biological and clinical knowledge leads us to the eradication of cancer and, by extension, of any terrible disease.