Research On State-modulating Drug Design Methods Based On System Dynamics

With the changes in economic society and ecological environment and the advent of an aging society,the spectrum of human diseases has undergone some changes.Nowadays,more complex diseases such as cancer,diabetes,Alzheimer's disease,depression,etc.,involve multi-gene interactions with the environment.Drug design methods based on reductionism are difficult to find the exact gene as the cause of the disease for targeting therapy.In addition,drug molecules screened based on reductionist concepts often show high activity and selectivity in in vitro experiments,but often have unsatisfactory efficacy or even toxic side effects when actually applied to humans in clinical trials,resulting in a low success rate of drug development.The reason for this is mainly due to the lack of holistic and dynamic considerations.Traditional Chinese medicine,with its unique theoretical system of "holistic concept and evidence-based treatment",has an advantage in the treatment of complex diseases,bringing hope and providing a different way of thinking from reductionism in the treatment of complex diseases.However,the problem of traditional Chinese medicine,is the lack of clarity of the medicinal substances and their mechanisms of action,which has been a constraint to convincing and international recognition.Traditional Chinese medicine,is embedded with the ideas of system science and has a natural connection with system science.Based on the "holistic concept and evidence-based treatment" of traditional Chinese medicine,this study proposes a new concept of drug design from the perspective of system science,called State-Regulation medicine.(1)The concept suggests that complex diseases fall into a stable state,which is the dynamic reason why they are difficult to treat effectively.Only by precisely regulating this disease steady state to eliminate or diminish it can we overcome the problems of complex diseases such as easy recurrence and side effects of drugs.(2)Based on this concept,this study further proposes a framework for the design of State-Regulation medicine:firstly,the macro-states and their transitions are used as a guide to construct micro-states that can characterize the macro-states,i.e.,molecular regulatory networks;then the molecular regulatory networks are modeled dynamically and the disease steady states are identified through systematic qualitative simulation;finally,the key targets are identified with the goal of eliminating or diminishing the disease steady states.The key targets identified by this system dynamics-based approach can reverse disease steady states and provide clues for drug design in complex diseases.(3)Based on the macro states in traditional Chinese medicine,a top-down design approach is adopted.Firstly,a system model of macro states and their transitions is constructed;secondly,the stable states expressed at the macros level are precisely the results of molecular interactions at the micro level.And therefore,the molecular regulatory networks of diseases at the micro level are constructed with the goal of characterizing specific macro states.The biomolecular mechanisms expressed at the micro level can be linked to the macro state,forming an integration of biomedicine and traditional Chinese medicine under a unified theoretical framework.So that the molecular regulatory network reflects the macro state characteristics and state transition principles.Taking cancer as an example,the cell cycle arrest state and apoptosis state regulated by p53 feedback network and the transition model between the two states are constructed.This molecular regulatory network can reflect the macroscopic state.(4)Dynamic modeling of disease molecular regulation network is carried out through discrete dynamic system Boolean model.In systems science,most of the states that appear during the dynamic evolution of the network are unstable.And due to the constraints of the regulatory interaction,these unstable states will produce shifts until they reach a stable state(attractor).This stable state in the face of some small perturbations will only transition in a certain region(attractor basin),and eventually will still return to the previous stable state,which has similar characteristics to the stable state of complex diseases.Therefore,by calculating the attractors in the system simulation,the steady state of complex diseases can be identified.And the basis for breaking the steady state can be provided.Taking the molecular regulatory network of breast cancer as an example,dynamic modeling was performed and its disease attractor states were calculated.And the system simulation results showed that the calculated attractors matched the data of the abnormal proliferation gene expression profile of breast cancer.Therefore,the disease attractor identified based on the system dynamics approach can characterize the disease steady state.(5)Using the dynamic model of the disease molecular regulatory network as a vehicle,system perturbation was performed for nodes or edges in the network to simulate drug intervention on the disease state.And whether the intervention strategy could be used as a key target by comparing the stable state in the system before and after the intervention.Taking the dynamic model of breast cancer molecular regulatory network as an example,the key targets for treating the disease are identified by system perturbation with the goal of transforming the abnormal proliferation state into a cell cycle arrest state or an apoptosis state.The system simulation results showed that the key targets identified by single-node inhibition were AKT,Wip1,and Cyc G.The key targets identified by two-node inhibition were MDM2+MDMX,MDM2+Wip1,MDM2+ Cyc G,Wip1+AKT,and Cyc G+AKT.Based on whether the key targets are mutated genes in the disease network,they are further classified into mutant and wild-type targets.The simulation results suggest that the inhibition of wild-type targets may be more beneficial for disease healing,which is consistent with previous studies and demonstrates the reliability of the method.Thus the key targets identified based on system dynamics can reverse the complex disease steady state and provide clues for drug design in complex diseases.In summary,based on the current problems faced in drug design for complex diseases and the inspiration of traditional Chinese medicine,this study firstly proposed the concept of State-Regulation medicine.And based on the characteristics of this concept,a system dynamics-based State-Regulation medicine design method was constructed.The key targets identified by this method that can change the state of breast cancer cells are basically consistent with previous studies.And the validity and reliability of the method are demonstrated.The State-Regulation medicine concept proposed in this study is based on the holistic level and system dynamics characteristics of the organism,which is advantageous in the classification and drug development of complex diseases.It introduces the traditional Chinese medicine concept focusing on overall dynamic regulation to biomedicine which concentrates on local microscopic studies such as molecular mechanisms.And provides a meeting point for the integration of traditional Chinese medicine and biomedicine.It is also beneficial to provide new solutions for target identification,drug discovery and drug combination design for current complex diseases faced.