HIV Drug Resistance: Current Status/Future Direction Brendan Larder - - PowerPoint PPT Presentation

HIV Drug Resistance: Current Status/Future Direction

Brendan Larder PhD

Chair of the RDI Scientific Core Group

Resistance testing today

• Resistance testing has become routine in

HIV management

– especially for ‘difficult’ cases

• Genotyping & phenotyping technology

has considerably improved & is widely commercially available

– Genotyping is much more commonly used: more rapid & cheaper than phenotyping – Commercial, FDA approved sequencing kits are available

• Interpretation is the key issue

– It is likely that interpretation problems limits the use of resistance testing

However….

Phenotyping

• Not technically feasible to assess multiple drug

combinations

– Any potential drug interactions not seen

• Cut-offs are an issue - how should they be

derived & used?

– ‘Technical’ cut-offs - limit of assay variability – ‘Biological’ cut-offs - natural variation in virus from untreated patients – ‘Clinical’ cut-offs: categorical, based on small numbers & specific to drug context

Interpreting resistance mutations

• Expert view & panels
• ‘Rules-based’ algorithms from consensus
• Rules driven software
• Phenotype-genotype database matching
• Predicting viral phenotype using artificial

intelligence

Generation of simple rules

• 215Y/F

= AZT resistance

• 184V/I

= 3TC resistance

• 103N

= EFV resistance

• 30N

= NFV resistance

• 50V

= APV resistance

Rules have become more complex…

• ABC resistance = 215Y/F + 1 or more of: 65R, 69D, 74V,

70R, 115F, 210W, 219E/Q, 184V/I + 1 or more of: 41L, 67N

• 3TC resistance = 44D + 118I + 4 or more of: 41L, 67N,

69D, 70R, 210W, 215F/Y, 219E/Q

• EFV resistance = 181C + any of: 100I, 101E, 179D, 230L
• NFV resistance = 82A/F/T + 2 or more of: 10I/R/V, 20M/R,

36I, 54L/M/V, 71V/T

• SQV resistance = 84V + 2 or more of: 10I/R/V, 20M/R, 36I,

54L/M/V,71V/T

How well do algorithms predict phenotype?

Error rates for protease inhibitors

Korn et al Scottsdale, 2001

%

5 10 15 20 25 30 IDV SQV RTV NFV APV Geno2pheno RetroGram Stanford

Korn et al Scottsdale, 2001

Error rates for RT inhibitors

%

5 10 15 20 25 30 35 40 ZDV DDC DDI D4T 3TC ABC NVP DLV EFV Geno2pheno RetroGram Stanford

• ‘Virtual Phenotype’

– Requires a substantial genotype-phenotype database – Based on mutation pattern recognition & phenotype retrieval – Relies on analysis of pre-defined mutational clusters, e.g., 41 + 67 + 210 + 215 in RT

• Neural networks

– Large data-sets are needed for training & testing

Predicting phenotype:

systematic approaches

Correlation of actual with virtual PT

Observed Phenotype (log10) Virtual Phenotype (log10)

(Correlation coefficients: 0.86 - 0.89)

n = 500 per group (random selection)

Neural networks

• Computer learning technique, where the network

learns to connect complex data & identify patterns by being ‘fed’ many examples

• Networks are ‘trained’ with large datasets
• Can be used to relate resistance mutations to

phenotype or clinical response

• Neural networks are particularly useful to analyse

resistance because of the many combinations of mutations that are possible

Predicting Lopinavir phenotype using neural networks

28-Mutation model

(n=1322)

R2 = 0.88

• 1
• 0.5

0.5 1 1.5 2 2.5

• 1
• 0.5

0.5 1 1.5 2 2.5

Actual fold (log)

Predicting D4T phenotype using a 26- mutation neural network model

y = 0.6705x + 2.0149

R2 = 0.6766

5 10 15 20 25 30 5 10 15 20 25 30

Actual fold increase

How well do rules predict clinical response?

Response to ABC at Week 4 by Stanford Rules

20 40 60 80 100

Sensitive (N=18) Contributes/Low (N=102) Resistant (N=46)

<400 copies > 0.5 log No Response

Lanier et al Scottsdale, 2001

(analysis = drop outs as failures)

Hammer et al Scottsdale, 2001

Standarised comparison of different rules

(odds ratio of virological failure)

The next logical step …….

• Develop large database(s) to correlate

mutation patterns with clinical responses

– Not just a correlation of genotype with phenotypic drug resistance – Addresses response to combinations of drugs

HIV Resistance-Response Database Initiative (RDI)

“To improve the clinical management of HIV infection by developing & making freely accessible a large clinical database & bioinformatic techniques that define with increased precision & reliability the relationships between drug resistance & virologic response to treatment”.

Aim:

RDI status

• Independent not-for-profit organization
• Scientific Core Group established
• Small executive group running analyses
• Range of large cohorts committed to support

with data

• Range of experts in the field pledged support

– e.g. Julio Montaner, Rob Murphy, Joep Lange, Brian Gazzard, Bonaventura Clotet, Jose Gatell

RDI approach

• Collecting genotype, treatment & clinical
• utcome data from large numbers of

patients

• Variety of data analysis methodologies

to relate resistance to clinical response

• Wide access to enable the database to

be queried via the internet

• Data identified from about 7,000 patients

– More when additional genotyping performed

• Database now: 1,000 patients from BC Centre,

Italian cohort, US Military, NIAID

• Power calculations performed to estimate

number of required data points

• Neural network models constructed & tested

Status of data analysis

Mutations Therapies

Neural Network Training Initial neural network model

BL VL On therapy VL

Initial neural network model

Mutations Therapies BL VL Predicted VL change

Neural Network Training

• Input variables

– 20 PI & 29 RT codon positions (based on prevalence)

• 12 drugs
• Therapy duration
• Output variable

– Viral load change at on-therapy time points (up to 6 months)

Initial neural network model

Wang et al Seville, 2002

Example result of NN model

Wang et al Seville, 2002

Training set (n=639)

R

2 = 0.85

• 4
• 2

2 4

• 4
• 3
• 2
• 1

1 2 3 Actual viral load change P re dic te d vira l loa d c ha nge

Wang et al Seville, 2002

Validation set (n=63)

R2 = 0.55

• 4
• 3
• 2
• 1

1 2 3

• 4
• 3
• 2
• 1

1 2 3 Actual viral load change Predicted viral load change

Example result of NN model

Predicting VL trajectory

Time Viral Load

NN Prediction:

75%±1.8% correct (86/115)

Wang et al Seville, 2002

Predicting VL failure

(Dichotomous Model, <, >400 copies)

<400

NN Prediction:

82%±1.6% correct

Wang et al Seville, 2002

Time Viral Load

Example of predicting response

• Baseline genotype for a ‘virtual

patient’

• RT: 41, 67, 118, 210, 215
• PI: 10, 46, 82, 90
• Alternative therapy regimens
• a. D4T, ddI, Kaletra
• b. D4T, ddI, indinavir
• c. AZT, 3TC, Kaletra
• d. AZT, 3TC, indinavir

Wang et al Seville, 2002

• 2
• 1

1 2 3

Baseline week 8 week 16

Viral Load (log10)

D4T/ddI/IDV D4T/ddI/Kal AZT/3TC/Kal AZT/3TC/IDV

Wang et al Seville, 2002

Example of predicting response

• Resistance rules & algorithms have increased in

complexity

– BUT these are not systematically designed to predict clinical response

• Methodologies such as neural networks can

enhance the accuracy of predictions

• RDI is establishing relationships between

genotype & virological response to combination therapy via analysis of a large clinical dataset

– Approaches of this nature are likely to improve the accuracy of predicting outcome from genotype

Summary