@article{GopalakrishnanMontazeriMenzetal.2014,
  author    = {Gopalakrishnan, Sathej and Montazeri, Hesam and Menz, Stephan and Beerenwinkel, Niko and Huisinga, Wilhelm},
  title     = {Estimating HIV-1 fitness characteristics from cross-sectional genotype data},
  series = {PLoS Computational Biology : a new community journal},
  volume    = {10},
  journal   = {PLoS Computational Biology : a new community journal},
  number    = {11},
  publisher = {PLoS},
  address   = {San Fransisco},
  issn      = {1553-734X},
  doi       = {10.1371/journal.pcbi.1003886},
  pages     = {14},
  year      = {2014},
  abstract  = {Despite the success of highly active antiretroviral therapy (HAART) in the management of human immunodeficiency virus (HIV)-1 infection, virological failure due to drug resistance development remains a major challenge. Resistant mutants display reduced drug susceptibilities, but in the absence of drug, they generally have a lower fitness than the wild type, owing to a mutation-incurred cost. The interaction between these fitness costs and drug resistance dictates the appearance of mutants and influences viral suppression and therapeutic success. Assessing in vivo viral fitness is a challenging task and yet one that has significant clinical relevance. Here, we present a new computational modelling approach for estimating viral fitness that relies on common sparse cross-sectional clinical data by combining statistical approaches to learn drug-specific mutational pathways and resistance factors with viral dynamics models to represent the host-virus interaction and actions of drug mechanistically. We estimate in vivo fitness characteristics of mutant genotypes for two antiretroviral drugs, the reverse transcriptase inhibitor zidovudine (ZDV) and the protease inhibitor indinavir (IDV). Well-known features of HIV-1 fitness landscapes are recovered, both in the absence and presence of drugs. We quantify the complex interplay between fitness costs and resistance by computing selective advantages for different mutants. Our approach extends naturally to multiple drugs and we illustrate this by simulating a dual therapy with ZDV and IDV to assess therapy failure. The combined statistical and dynamical modelling approach may help in dissecting the effects of fitness costs and resistance with the ultimate aim of assisting the choice of salvage therapies after treatment failure.},
  language  = {en}
}
@article{FrontonPilariHuisinga2014,
  author    = {Fronton, Ludivine and Pilari, Sabine and Huisinga, Wilhelm},
  title     = {Monoclonal antibody disposition: a simplified PBPK model and its implications for the derivation and interpretation of classical compartment models},
  series = {Journal of pharmacokinetics and pharmacodynamics},
  volume    = {41},
  journal   = {Journal of pharmacokinetics and pharmacodynamics},
  number    = {2},
  publisher = {Springer},
  address   = {New York},
  issn      = {1567-567X},
  doi       = {10.1007/s10928-014-9349-1},
  pages     = {87 -- 107},
  year      = {2014},
  abstract  = {The structure, interpretation and parameterization of classical compartment models as well as physiologically-based pharmacokinetic (PBPK) models for monoclonal antibody (mAb) disposition are very diverse, with no apparent consensus. In addition, there is a remarkable discrepancy between the simplicity of experimental plasma and tissue profiles and the complexity of published PBPK models. We present a simplified PBPK model based on an extravasation rate-limited tissue model with elimination potentially occurring from various tissues and plasma. Based on model reduction (lumping), we derive several classical compartment model structures that are consistent with the simplified PBPK model and experimental data. We show that a common interpretation of classical two-compartment models for mAb disposition-identifying the central compartment with the total plasma volume and the peripheral compartment with the interstitial space (or part of it)-is not consistent with current knowledge. Results are illustrated for the monoclonal antibodies 7E3 and T84.66 in mice.},
  language  = {en}
}