# Model Predictive Control and Its Role in Biomedical Therapeutic Automation: A Brief Review
## 3 MPC in Biomedical Applications
### Cancer  
#### 3.5.1 Oncolytic Viral Therapy  
Oncolytic viral therapy (OVT) is an emerging cancer treatment in which genetically modified viruses are employed to cure cancer in such a way that the healthy cells are not harmed or infected in any way [115, 116]. To infect and kill cancer cells, the OVT either induces immunological responses against them or produces lysis, which involves rupturing cancer cell membranes by extensively replicating the oncolytic virus within them. Virus particles that are released as a result of OVT are disseminated to spread the infection to tumour cells that are vulnerable [115, 116, 117]. Considering its numerous positive effects on tumour reduction, OVT is widely recognised as an acceptable cancer therapy that can be safely applied in association with other treatments, such as surgeries. OVT treatment can also be applied on its own without any associated therapies to shrink tumors. However, a number of technical, biochemical, immunological, and clinical factors impede OVT performance. The process of selecting the most effective oncolytic virus for a certain malignancy, determining and controlling the virus dosage supplied to the cancer cells as well as developing suitable protocols is time-consuming and challenging. Progress in virus engineering has benefited OVT by furnishing newer variants of genetically modified oncolytic viruses to act against target tumor cells while remaining neutral against healthy cells [115, 116]. Researchers have explored various modifications to boost virus survivability when confronted with the immune system. For instance, a study [116] reported that the ADPEGHER virus was generated by coating a naked adenovirus with a non-immunogenic polymer such as polyethylene glycol (PEG), together with the antibody herceptin. In comparison to the earlier version, this modified adenovirus improved infection, tumour regression, and caused no harm to healthy cells. It is crucial to maximise the infectivity and anti-tumor activity of the oncolytic viruses. In order to reach this goal, OVT effectiveness enhancement has been investigated using mathematical modelling. Model-based investigations have revealed mechanistic insights into the multifarious cancer cell dynamics and oncolytic virus actions within the host, both in the presence and absence of immune responses [118, 119, 120]. Researchers [118] also studied the effects of OVT in mice using mathematical modeling. Their model provided an excellent fit to the temporal data of tumour given in a previous study [116]. A viable in silico platform and exact biological parameters were reported by other investigators [118], which can be used to further investigate potential OVT protocols. Recent developments in the modelling of OVT dynamics and viral genetic engineering made it possible to estimate the ideal viral dosages that should be provided in order to maximise the efficacy of the therapeutic interventions. However, OVT has not been substantially investigated in the context of control theory. Particularly in the context of model predictive control (MPC), the ideal sequence of inputs is determined by minimising a cost function over the course of a prediction horizon while also meeting system limitations. The virus input and the positivity of states both serve as restrictions in OVT. Moreover, because the input is of limited duration in terms of sampling time, the OVT therapy may be considered as a form of impulsive control system (ICS) [121]. As a result, a continuous or discrete virus injection estimate tends to be less accurate than a corresponding impulsive representation [121, 122]. Impulsive control solutions have been developed for a variety of biomedical applications such as influenza [123, 124], type 1 diabetes therapy [125], HIV [126] and many more. There have been several studies on ICS in the MPC environments [127]. In order to compensate for the effects of plant-model mismatch, researchers [128] created an impulsive offset-free MPC that was not dependent on the plant model.