# Model Predictive Control and Its Role in Biomedical Therapeutic Automation: A Brief Review
## 3 MPC in Biomedical Applications
### Hiv  
Pharmaceutical treatment for HIV infection increases the patient's life expectancy and enhances the patient's overall quality of life. Research studies have explored therapy regimens that stimulate HIV-specific immune responses in order to avoid side effects and drug resistance associated with long-term drug administration [103]. With current medicines, it is almost impossible to completely eradicate the virus from the body because of the presence of hidden reservoirs of the virus that have a longer half-life (from months to years). For instance, a decrease in specific cytotoxic T lymphocytes (CTLs) has been linked to HIV progression; however, patients with slow disease progression exhibit high CTLs as compared to those with rapid disease progression. Reverse transcriptase and protease inhibitors are two types of highly active antiretroviral medications that restrict the formation of virions by blocking the creation of viral protein precursors and protease inhibitors. However, the effectiveness of such highly active antiretroviral therapy (HAART) has been found to be restricted. To mitigate the severe side effects and development of drug resistance associated with long-term medication administration, recent research efforts have focussed on designing therapy regimens that can increase HIV-specific immune responses [103; 104]. Structured treatment interruptions (STIs) have been shown to be beneficial in enhancing HIV immunity in a number of clinical investigations, particularly when conducted immediately after infection [105]. STIs have been found in a number of studies to increase immunological responses to the HIV virus. STD (sexually transmitted disease) treatment is helpful when begun within the first few weeks of HIV infection but tends to be ineffective when begun later in infection [106; 107; 108; 109; 110; 111]. The qualitative influence of medication side effects on HIV dynamics has also been detailed in a number of clinical investigations, establishing the significance of feedback control systems in such cases. MPCs have emerged as one of the best solutions for the optimal control of STI among different control strategies, owing to their inherent resistance to noise, uncertainty in their model shape and their capacity to successfully deal with dynamic constraints. Appropriate model selection is of paramount importance in the development of an STI MPC application. Researchers have chosen the [PERSON] and Nowak models [105; 112; 113] for this purpose since these models most closely resemble the natural evolution of HIV as hypothesised in [114]. Investigators have also proposed that two MPC algorithms be used to calculate the \"optimal\" doses of PI and RTI medications over a common prediction horizon. In this methodology, the simulation results of both algorithms are reviewed and compared to one another [110]. In this approach, all state variables were tested weekly, but only CD4+ T concentration and VL (virus level in the plasma) were measured once a month. CD4+ T helper cells are white blood cells that are an essential part of the human immune system. They are often referred to as CD4 cells, T-helper cells or T4 cells. Their main role is to send signals to other types of immune cells, including CD8 killer cells, which then destroy the infectious particle. Rather than employing a correction strategy in the presence of model flaws, the above-mentioned method maintained control variables within their prescribed ranges by augmenting the system states over successive iterations. As per [110], the HIV therapy can be adjusted using the MPC algorithm to produce the \"safe\" stable state that exists even when no therapy is applied. Hence, the immune system can achieve a stable state even in the absence of therapy. However, the internal model is unable to reach a perfect stable state because it is unable to zero the last state-variable derivative.