Approaching Pharmacometrics as a Paleontologist Would: Recovering the Links Between Drugs and the Body Through Reconstruction

Exploring an alternative explanation for the second phase of viral decay: Infection of short-lived cells in a drug-limited compartment during HAART

Most HIV-infected patients who initiate combination antiretroviral therapy experience a viral load decline in several phases. These phases are characterized by different rates of viral load decay that decrease when transitioning from one phase to the next. There is no consensus as to the origin of these phases. One hypothesis put forward is that short- and long-lived infected cells are responsible for the first and second phases of decay, respectively. However, significant differences in drug concentrations are observed in monocytes from various tissues, suggesting the first two phases of decay in viral loads could instead be attributed to short-lived cells being differently exposed to drugs. Compared to a well-exposed compartment, new cell infection can be expected in a compartment with limited drug exposure, thus leading to a slower viral load decay with potential virologic failure and drug resistance. In the current study, the latter hypothesis was investigated using a model of viral kinetics. Empirical datasets were involved in model elaboration and parameter estimation. In particular, susceptibility assay data was used for an in vitro to in vivo extrapolation based on the expected drug concentrations inside physiological compartments. Results from numerical experiments of the short-term evolution of viral loads can reproduce the first two phases of viral decay when allowing new short-lived cell infections in an unidentified drug-limited compartment. Model long-term predictions are however less consistent with clinical observations. For the hypothesis to hold, efavirenz, tenofovir and emtricitabine drug exposure in the drug-limited compartment would have to be very low compared to exposure in peripheral blood. This would lead to significant long-term viral growth and the frequent development of resistant strains, a prediction not supported by clinical observations. This suggests that the existence of a drug-limited anatomical compartment is unlikely, by itself, to explain the second phase of viral load decay.

A Mathematical Model to Predict HIV Virological Failure and Elucidate the Role of Lymph Node Drug Penetration

Preventing virological failure following HIV treatment remains a difficult task that is further complicated by the emergence of drug resistance. We have developed a mathematical model able to explain and predict HIV virological outcomes for various compounds and patients' drug intake patterns. Compared to current approaches, this model considers, altogether, drug penetration into lymph nodes, a refined adherence representation accounting for the propensity for long drug holidays, population pharmacokinetic and pharmacodynamic variability, drug interaction, and crossresistance. In silico results are consistent with clinical observations for treatment with efavirenz, efavirenz in association with tenofovir DF and emtricitabine, or boosted darunavir. Our findings indicate that limited lymph node drug penetration can account for a large proportion of cases of virological failure and drug resistance. Since a limited amount of information is required by the model, it can be of use in the process of drug discovery and to guide clinical treatment strategies.

Towards Quantitative Systems Pharmacology Models of Chemotherapy-Induced Neutropenia

Neutropenia is a serious toxic complication of chemotherapeutic treatment. For years, mathematical models have been developed to better predict hematological outcomes during chemotherapy in both the traditional pharmaceutical sciences and mathematical biology disciplines. An increasing number of quantitative systems pharmacology (QSP) models that combine systems approaches, physiology, and pharmacokinetics/pharmacodynamics have been successfully developed. Here, I detail the shift towards QSP efforts, emphasizing the importance of incorporating systems-level physiological considerations in pharmacometrics.