Poster presentation at:
4th International Congress on Drug Therapy in HIV Infection
Glasgow, UK; 17-21 November 2002
A COLLABORATIVE HIV RESISTANCE RESPONSE DATABASE INITIATIVE: PREDICTING
VIROLOGICAL FAILURE USING NEURAL NETWORK MODELS
D Wang & B.A. Larder. On Behalf of The HIV Resistance Response
Database Initiative (RDI)
The goal of the RDI is to create a sufficiently large relational database
to correlate baseline HIV-1 resistance genotypes and drug therapy to
virological response. Initially, we wished to develop neural network
(NN) models to predict virological failure.
Baseline genotype, viral load (VL) and treatment data, plus week 24
VL from clinical studies is being collected in a customised Oracle database.
We plan to collect data from 5000 - 10,000 individuals. Initial NN models
were based on complex (12 drugs and 49 mutations) or simplified (6 drugs
and 31 mutations) data input parameters. Patient samples from the Vigilance
II trial were used for NN training. The VL input was either the continuous
variable of absolute VL change from baseline, or a dichotomous variable
of virological success or failure. Virological failure was defined as
VL>400 copies/ml at week 24.
Training of both NN models using 700 patient cases was surprisingly
successful. For the complex NN model, the correlation between the predicted
and actual absolute VL change for the training set gave an value
of 0.85 (p<0.0001). In independent test and validation sets, this
correlation gave an average value of 0.5±0.05 (p<0.0001).
The VL trajectory was correctly predicted in 75% (±1.8%) of cases
in the test sets by the complex NN model. Of interest, the simplified
NN model was slightly less accurate in predicting absolute VL change
(average = 0.47±0.05 for test and validation sets) or the
VL trajectory (72±1.8% correct for the test and validation sets).
Correct prediction of failure based on baseline genotype and treatment
was achieved in 100% of cases in the training data set and an average
of 82%±1.6% of cases in multiple cross validation data sets.
This model was also used successfully to predict 'in silico' VL failure
using genotypes and a variety of hypothetical treatment regimens.
Initial development of NN models resulted in successful training with
a limited dataset and demonstrated the feasibility of this approach
to predict absolute VL change, VL trajectory and virological failure
from complex input variables. As the size of the training data set increases,
it is anticipated that the accuracy of these predictions will also increase
and that this technique will prove to be a useful tool for predicting
treatment outcome from genotype.