386746 Optimization of HIV Treatment Schedules Based on the Mitochondrial Toxicity of Nucleoside-Analogue Antiretroviral Agents

Tuesday, November 18, 2014: 5:03 PM
401 - 402 (Hilton Atlanta)
Premanand A. Srinivasan and Antonios Armaou, Chemical Engineering, The Pennsylvania State University, University Park, PA

Nucleoside-analogue Reverse Transcriptase Inhibitors (NRTIs) are an important class of drugs used for the treatment of HIV infection. Although these drugs are effective in maintaining the viral load below detectable limits, their prolonged use leads to several medical complications. Clinical and molecular biological evidences suggest that NRTIs cause toxic side-effects by interfering with normal mitochondrial function. Since NRTIs are structurally similar to the natural deoxynucleotides (dNTPs), they competitively inhibit the mitochondrial DNA (mtDNA) replication and transcription processes. These negative effects lead to a depletion of mtDNA and mRNA transcripts and therefore a reduced expression of proteins for the electron transport chain.

To minimize drug toxicity and drug expenses, structured interruptions in treatment are widely practiced. Mathematical models describing the disease dynamics have been employed to minimize the intake of drug by optimizing these interruptions. However, it is of utmost importance to ensure that the drug intake is within the toxic threshold. To achieve this, a mathematical model that provides mapping between the NRTI drug concentration and mitochondrial protein levels is necessary. We developed a hybrid model of the mitochondrial gene expression process that accounts for the effects of NRTIs. The copy numbers of mtDNA and mRNA and the protein concentrations are tracked using a deterministic system of ordinary differential equations, while the parameters that depend on the drug concentration are computed from repeated event-based stochastic simulations. Since NRTIs are known to inhibit the rate of mtDNA replication and transcription, the corresponding parameters are obtained through stochastic simulations of the respective processes. These processes are polymerase chain reactions and they are modeled and simulated using Gillespie’s algorithm, a kinetic Monte Carlo method. At each location on the mtDNA strand, the polymerases can perform one of these actions: (1) addition of a nucleotide/NRTI (2) excision of the previously added block (3) Disassociation of the polymerase from the strand (4) Re-association of the polymerase to the strand. The simulation stops either when a strand is completely replicated or transcribed or when the polymerase disassociates and fails to re-associate. The occurrence of the latter is more probable when a NRTI or AVRN was added in the previous step and was failed to be removed. The training and validation datasets for the model are obtained experimentally.

An optimization problem is formulated to minimize the average maximal intracellular drug concentration with an inequality constraint on the mitochondrial protein concentration. This constraint is the protein threshold required for normal cellular function. Different treatment schedules that achieve similar reductions in average drug concentration are investigated.


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