Identifying nano-scale molecules for effectively treating a disease facing several fundamental challenges. The governing and interfacial mechanisms enable the transcending through 9 orders of magnitude separation in length scale are not clearly understood. In each living cell, the interactions among the bio molecules, proteins and nucleic acids, intrinsically serve as the foundation of the extensive networks of signal and regulatory pathways. Emergent cellular functionalities are derived from the self-organization of these pathways and cannot be easily related to individual bio-molecular interactions. As such, the sheer magnitude of pathway processes and pathway crosstalk presents significant challenges to the manipulation of cellular functionalities. The phenotypic and genotypic outcomes are the integral outcomes translating from cell, organ to human body. The information space spanning nano-scale molecules to meter-scale human body is a mostly unknown big-data set. With these challenges, the nanomedicine endeavor of screening potential drug molecules, search for the bio targets and going through in vitro as well as in vivo tests becomes a high-cost and long-time investment.
In addition, almost all diseases are not cause by a single aberrant pathway. We must resort to use combination of several drugs to attack the problem from many fronts. When we harness the rational design of combinatorial drug therapy modalities to stimulate these cellular pathways with improved efficacy and low toxicity, this imposes another challenge which pertains to another large parameter space. For example, 6 drugs with 10 concentrations each would result in 1,000,000 potential search trials. In the case of developing combinatorial medicine, we face the difficulty of handling two big-data sets, bio system and drug combination, and their interactions.
We do need to have some knowledge to correctly diagnose the disease and develop drugs to better treat diseases. On the other hand, if just collecting data without discretion, we can be drowned in the ocean of big-data. At here, we take an unorthodox interdisciplinary approach to bypass the need of directly handling mostly unknown big-data sets. This method can transform the way that nanotherapy is administered and remove its barriers to acceptance is feedback system control (FSC), a top-down approach that directs a bio system, cell or body, toward a desired phenotype. When drug compounds with arbitrarily decided concentrations are applied to cells, their signaling pathways respond unpredictably. If specific system outputs are not met (e.g. optimal apoptosis, optimal safety, etc.), FSC uses an engineering search algorithm that selects the next group of drugs to iteratively feedback to the bio system. FSC is able to rapidly achieve desired phenotypes, even amongst a prohibitively large testing parameter space.
FSC consists of 4 modules. Let us take the viral infection project as an example. The first module is the input stimulations, e.g. the drug combinations. The second module is the bio system of interest, e.g. virus and host cell. The third module is the objective system readouts, e.g. the percentage of infected host cells, and/or toxicity/side effects. The fourth module is the search algorithm, which provides the next set of stimulants and dosages for directing the biological system toward the desired state (Fig. 1).
FSC technique has been successfully demonstrated in 3 inhibition of viral infections, 6 eradication of cancers and maintenance of human embryonic stem cells. The key discovery of applying FSC to control bio system is that the response surface of the complex system to the multi input stimulants are very smooth, which results in needing only a very small number of searches. With this finding, we can very rapidly model the animal and clinical tests with only a few tests and achieve the most potent drug-dose combinations. With these experiences, we are in the process of developing personalized medicine by optimizing combinatorial drugs based on patient’s sample. Such that we will be able to prescribe drugs based on specific patient not just based on the type of disease.
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