An engineering approach to translational medicine

The mechanisms that drive disease have long confounded the medical industry. How much of a part does genetics play? How does the patient’s lifestyle influence his or her risk of developing cancer? What makes one patient respond favorably to a particular treatment, while another suffers severe side effects? The healthcare approach known as personalized medicine, which is gaining popularity in clinical circles, hopes to answer these questions. Personalized medicine (also called pharmacogenomics) sees disease as an evolving process that is influenced by the patient’s genetic makeup, environment, and lifestyle. Factors such as physical activity, smoking, and alcohol consumption may modify the disease’s progression. Dr. Michael N. Liebman, President of Strategic Medicine, contrasts this approach with traditional medicine in a 2005 article in The American Scientist. According to Liebman, traditional medicine focuses on a “bottom-up” approach that entails collecting data and looking for patterns. Liebman argues that we know too little about complex systems to make such generalizations, and that time spent mining data distracts scientists from building a predictive model of the disease. Personalized medicine, he says, has a “top-down” approach similar to that of engineers: taking a system apart and figuring out how each component can be used to develop a patient-specific treatment. Instead of focusing on the disease at one point in time, this approach treats the patient as a system that has been acted upon by many elements at different points in time. The goal of this wider scope is to determine causality, which will help to develop effective treatment options for the patient while minimizing risk. Small genetic variations may determine whether a patient responds positively to a medication or has life-threatening side effects. If a particular gene increases the risk of an adverse reaction, doctors can look for alternative treatment options. According to Liebman, this benefits both the patient and the drug companies. Pharmaceutical companies may be able to avoid large-scale safety issues such as occurred with Vioxx. Building a chronological record of the patient’s life is an important component of personalized medicine. Physicians record the patient’s family history, personal history of diagnoses and illnesses, treatments and response, and lifetime exposure to risk factors. In a sense, the patient’s disease becomes a vector projected in multidimensional space. This record is particularly useful for diseases such as breast cancer, since the breast develops continuously from birth to old age. Another important element of this approach is stratification—looking at how patients in a particular subgroup manifest disease and respond to treatment. People in these subgroups are similar to each other, but not to those in other subgroups. For example, a 2002 study of menopause stratified women using genetic and environmental factors to assess their risk of developing post-menopausal diseases such as breast cancer and osteoporosis. Liebman argues that genetic testing alone is inadequate because it doesn’t address the complexity of the patient’s system: It can note correlations, but it can’t determine the mechanisms behind them.  In other words, it can explain the “what,” but not the “how.” Some modeling types, such as stochastic activity networks or hybrid Petri networks, have the potential to integrate this data into a more advanced system that supports qualitative reasoning about the disease-causing factors. Some companies are already developing such models. Strategic Medicine and BIOBASE are collaborating to analyze the complex biological networks occurring at the protein and genetic level. London-based InforSense has developed large-scale data analysis software that can record a patient’s history and search for patients with similar disease patterns. Concentia Digital’s software has the capability to track diagnostic and research images. These are the first steps down what is most likely a long path. Pharmacogenomics is still in its early developmental stages, and sifting through the vast wealth of data to fully determine the genetic and environmental combinations that lead to disease will take years of effort. Strategic Medicine, Inc (SMI) offers disease stratification products and services driven by clinical need and supported by its rapidly expanding knowledge base and improved implementation of diagnostic technology. These products sit on top of, and interact directly with existing electronic medical records (EMR). They can also function as stand-alone systems and are accessible by patients through their healthcare providers. SMI works with patient advocacy groups, disease foundations, and pharma clinical research teams to establish better guidelines for therapy and care, and develop better predictors of outcome. These are implemented in the company’s disease model systems. SMI is able to develop models that are of immediate relevance to the patient, their healthcare providers and payers alike. Patients can access these services through their physicians much the same way today’s molecular diagnostic products are accessed. SMI’s products will make a direct contribution to the patient’s outcome and management. The article below, from 2005, presents some of the earlier foundation on which Strategic Medicine has evolved. For questions or comments, please visit or email Translational Medicine

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