Evolutionary game theoretic strategy for optimal drug delivery to influence selection pressure in treatment of HIV-1

Untethered Microrobots for Active Drug Delivery: From Rational Design to Clinical Settings

Recent advances of untethered microrobots, which navigate the complex regions in vivo for therapeutics, have presented promising multiple applications on future healthcare. Microrobots used for active drug delivery system (DDS) have been demonstrated for advanced targeting distribution, improved delivery efficiency, and reduced systemic side effects. In this review, the therapeutic benefits of active DDS are presented compared to the traditional passive DDS, which illustrate the historical reasons for choosing active DDS. An integrated 5D radar chart analysis model containing the core capabilities of the active DDS is innovatively proposed. It would be a practical tool for measurement and mapping of the field of active delivery, followed by the evolutions and bottlenecks of each technical module. The comprehensive consideration of microrobots before clinical application is also discussed from the aspects of robot ethics, dosage, quality control and stability control in actual production. Gastrointestinal and blood administration, as two major clinical scenes of drug delivery, are discussed in detail as examples of the potential bedside applications of active DDS. Finally, combined with the reported analysis model, the current status and future outlook from the translation prospect to the clinical scenes of microrobots are provided.

A study on the dynamics of temporary HIV treatment to assess the controversial outcomes of clinical trials: An in-silico approach

It is still unclear under which conditions temporary combined antiretroviral therapy (cART) results in a prolonged remission after interruption. Clinical trials have contradicting reposts about the effect of cART during primary HIV infection on the disease progression. Here we propose that the apparent contradiction is due the presence of a window of opportunity for cART treatment observed in the in silico studies. We study non-linear correlations in the HIV dynamics over time using information theory. This approach requires a large dataset of CD4+ T lymphocytes and viral load concentrations over time. Since it is unfeasible to collect the required amount of data in clinical trials we use C-ImmSim, a clinically validated in silico model of the HIV infection, to simulate the HIV infection and temporary cART in 500 virtual patients for a period of 6 years post infection in time steps of 8 hours. We validate the results of our model with two published clinical trials of temporary cART in acute infection and analyse the impact of cART on the immune response. Our quantitative analysis predicts a “window of opportunity” of about ten months after the acute phase during which a temporary cART has significantly longer-lasting beneficial effects on the immune system as compared to treatment during the chronic phase. This window may help to explain the controversial outcomes of clinical trials that differ by the starting time and duration of the short-term course cART and provides a critical insight to develop appropriate protocols for future clinical trials.

HIV-1-infected T-cells dynamics and prognosis: An evolutionary game model

BACKGROUND AND OBJECTIVE

Understanding the dynamics of human immunodeficiency virus (HIV) is essential for depicting, developing, and investigating effective treatment strategies. HIV infects several types of immune cells, but its main target is to destroy helper T-cells. In the lymph nodes, the infected T-cells interact with each other and their environment to obtain more resources. According to infectivity and replicative capacity of T-cells in the HIV infection process, they can be divided into four phenotypes. Although genetic mutations in the reverse transcription that beget these phenotypes are random, the framework by which a phenotype become favored is affected by the environment and neighboring phenotypes. Moreover, the HIV disease has all components of an evolutionary process, including replication, mutation, and selection.

METHODS

We propose a novel structure-based game-theoretic model for the evolution of HIV-1-Infected CD4+T-cells and invasion of the immune system. We discuss the theoretical basis of the stable equilibrium states of the evolutionary dynamics of four T-cells types as well as its significant results to understand and control HIV infection. The results include the importance of genetic variations and the process of establishing evolutionary dynamics of the virus quasispecies.

RESULTS

Our results show that there is a direct dependency between some parameters such as mutation rates and the stability of equilibrium states in the HIV infection. This is an interesting result because these parameters can be changed by some pharmacotherapies and alternative treatments. Our model indicates that in an appropriate treatment the relative frequency of the wild type of virus quasispecies can be decreased in the population. Consequently, this can cause delaying the emergence of the AIDS phase. To assess the model, we investigate two new treatments for HIV. The results show that our model can predict the treatment results.

CONCLUSIONS

The paper shows that a structured-based evolutionary game theory can model the evolutionary dynamics of the infected T-cells and virus quasispecies. The model predicts certain aspects of the HIV infection process under several treatments.

Assessing Uncertainty in A2 Respiratory Syncytial Virus Viral Dynamics

Respiratory syncytial virus (RSV) is the most common cause of bronchiolitis and pneumonia in children younger than 1 year of age in the United States. Moreover, RSV is being recognized more often as a significant cause of respiratory illness in older adults. Although RSV has been studied both clinically and in vitro, a quantitative understanding of the infection dynamics is still lacking. In this paper, we study the effect of uncertainty in the main parameters of a viral kinetics model of RSV. We first characterize the RSV replication cycle and extract parameter values by fitting the mathematical model to in vivo data from eight human subjects. We then use Monte Carlo numerical simulations to determine how uncertainty in the parameter values will affect model predictions. We find that uncertainty in the infection rate, eclipse phase duration, and infectious lifespan most affect the predicted dynamics of RSV. This study provides the first estimate of in vivo RSV infection parameters, helping to quantify RSV dynamics. Our assessment of the effect of uncertainty will help guide future experimental design to obtain more precise parameter values.

Determining drug efficacy parameters for mathematical models of influenza

Antivirals are the first line of defence against influenza, so drug efficacy should be re-evaluated for each new strain. However, due to the time and expense involved in assessing the efficacy of drug treatments both in vitro and in vivo, treatment regimens are largely not re-evaluated even when strains are found to be resistant to antivirals. Mathematical models of the infection process can help in this assessment, but for accurate model predictions, we need to measure model parameters characterizing the efficacy of antivirals. We use computer simulations to explore whether in vitro experiments can be used to extract drug efficacy parameters for use in viral kinetics models. We find that the efficacy of neuraminidase inhibitors can be determined by measuring viral load during a single cycle assay, while the efficacy of adamantanes can be determined by measuring infected cells during the preparation stage for the single cycle assay.

HIV reservoirs and immune surveillance evasion cause the failure of structured treatment interruptions: a computational study

Continuous antiretroviral therapy is currently the most effective way to treat HIV infection. Unstructured interruptions are quite common due to side effects and toxicity, among others, and cannot be prevented. Several attempts to structure these interruptions failed due to an increased morbidity compared to continuous treatment. The cause of this failure is poorly understood and often attributed to drug resistance. Here we show that structured treatment interruptions would fail regardless of the emergence of drug resistance. Our computational model of the HIV infection dynamics in lymphoid tissue inside lymph nodes, demonstrates that HIV reservoirs and evasion from immune surveillance themselves are sufficient to cause the failure of structured interruptions. We validate our model with data from a clinical trial and show that it is possible to optimize the schedule of interruptions to perform as well as the continuous treatment in the absence of drug resistance. Our methodology enables studying the problem of treatment optimization without having impact on human beings. We anticipate that it is feasible to steer new clinical trials using computational models.