A Tool for Predicting the Success of First-Line Antiretroviral - - PowerPoint PPT Presentation

▶
a tool for predicting the success of first line
SMART_READER_LITE
LIVE PREVIEW

A Tool for Predicting the Success of First-Line Antiretroviral - - PowerPoint PPT Presentation

Sep 03, 2022 265 likes •439 views

A Tool for Predicting the Success of First-Line Antiretroviral Therapies Alejandro Pironti Computational Biology and Applied Algorithmics Max Planck Institute for Informatics May 19, 2011 Motivation The first antiretroviral regimen:

slide-1
SLIDE 1

A Tool for Predicting the Success of First-Line Antiretroviral Therapies

Alejandro Pironti

Computational Biology and Applied Algorithmics Max Planck Institute for Informatics May 19, 2011

slide-2
SLIDE 2

May 19, 2011 Alejandro Pironti

Motivation

  • The first antiretroviral regimen:

– Best chance of sustained virological suppression – Selection under consideration of transmitted drug resistance – Simplicity and side effects also important for success

Primary Drug Resistance Trends in the RESINA Cohort

5 10 15 20 2001 2002 2003 2004 2005 2006 2007 2008

Year Prevalence(%)

slide-3
SLIDE 3

May 19, 2011 Alejandro Pironti

Motivation

  • Available tools for assisting therapy selection:

– Designed for therapy-experienced patients – Therapy-naïve patients:

  • Different mutations, e.g. T215Y →T215S
  • A subset of regimens
slide-4
SLIDE 4

May 19, 2011 Alejandro Pironti

The Data

  • 2074 first-line antiretroviral therapies from

EuResist and the RESINA cohort including:

– Drug compounds

Histogram of Drug Combinations

slide-5
SLIDE 5

May 19, 2011 Alejandro Pironti

The Data

  • 2074 first-line antiretroviral therapies from

EuResist and the RESINA cohort including:

– PR and RT sequences – A viral load measurement for week 48 (37-59) after therapy start or an earlier one if therapy failed before

slide-6
SLIDE 6

May 19, 2011 Alejandro Pironti

Dichotomization

  • Failure:

– VL > 50 cp/ml at week 48 (37-59) – Therapy stop before week 48

  • Definition yielded

– 1188 (57%) successes – 886 (43%) failures

  • Development set: 1854

therapies

  • Test set: 220 therapies

10 20 30 40 50 60 % Virological Failure at Week 48 Other Failure Success

slide-7
SLIDE 7

May 19, 2011 Alejandro Pironti

Prediction

  • Linear support vector

machine was used to predict therapy success

  • Training with

development set

  • Features:

– Genotype – Drug combination – Interactions up to 3rd

  • rder,

e.g. mutation:mutation:drug

  • Feature selection:

z-score ≥ 2

Schematic Representation of a Support Vector Machine

Hyperplane M a r g i n

slide-8
SLIDE 8

May 19, 2011 Alejandro Pironti

Performance

Test set. AUC = 0.7110 5-fold cross-validation on development set. AUC = 0.7135 Therapy-Naïve Tool 10 CV Development Set (AUC=0.7739) Test Set (AUC=0.7647) False Positive Rate True Positive Rate Evolutionary Engine (therapy-experienced tool) Therapy-Experienced Set (AUC=0.7650) Therapy-Naïve Set (AUC=0.4842) False Positive Rate True Positive Rate

slide-9
SLIDE 9

May 19, 2011 Alejandro Pironti

Selected Features

  • 438 out of 8090 features selected or included by default
  • Default features:

– WHO Mutations for Surveillance of Transmitted Drug Resistance – Drug Combination

  • Selected features:

– PR Mutations: 4S, 10I, 12K, 13V, 15L, 19I, 20R, 37D/T, 41K, 57K, 61D/H, 63Q/N/H, 64M, 67E, 74A, 82I – RT Mutations: 39A, 162A/G, 166I, 169D, 176S, 179D/I, 192N, 200A, 207G – 140 Drug-Mutation and 190 Drug-Drug Interactions

Mutations for which also a literature reference could be found

slide-10
SLIDE 10

May 19, 2011 Alejandro Pironti

Tool Development Status

  • Tool and web interface have been implemented
  • Tool will be part of geno2pheno[resistance]
  • Release scheduled together with

geno2pheno[resistance]’s upgrade from 3.2 to 3.3.

  • http://www.geno2pheno.org
slide-11
SLIDE 11

May 19, 2011 Alejandro Pironti

Example 1

  • Patient with viral mutations:

– PR: 3I, 37N, 57K, 63P, 65D, 77I – RT: 60I, 67N, 69D, 122K, 135T, 178I/M, 214F, 245Q, 248D, 272A, 286A, 288S, 296S, 297K, 311K/R

  • FLART: 3TC+ABC+EFV
  • Reached a VL of 56 cp/ml after

4 months of therapy. However, 2 months later, his VL went up to 4.6 log. He was switched to 3TC+ABC+FPV/r after which his VL was promptly suppressed.

RT: 60I, 65R, 67N, 69D, 122K, 135T, 178M, 181C, 190S, 214F, 245Q, 248D, 272A, 286A, 288S, 296S, 297K

Drug resistance mutations

slide-12
SLIDE 12

May 19, 2011 Alejandro Pironti

Example 1

slide-13
SLIDE 13

May 19, 2011 Alejandro Pironti

Example 2

  • Patient with viral mutations:

– PR: 3I, 37N, 39A, 41K, 60E, 63P, 71T, 77I, 93L – RT: 123E, 135V, 162C, 169D, 207E, 214F, 245M

  • FLART: 3TC+AZT+EFV
  • After 9 months of therapy the

patient still had a residual viral load of 135 cp/ml. The patient’s therapy was switched to 3TC+ABC+EFV after which the patient’s viral load became undetectable.

slide-14
SLIDE 14

May 19, 2011 Alejandro Pironti

Example 2

Originally selected FLART Sucessful, second-line ART

slide-15
SLIDE 15

May 19, 2011 Alejandro Pironti

Outlook

  • Inclusion of Darunavir
  • Use of

– Gag mutations – HLA types

for prediction improvement

slide-16
SLIDE 16

May 19, 2011 Alejandro Pironti

Acknowledgements

Max-Planck-Institut für Informatik

Thomas Lengauer André Altmann Joachim Büch Alexander Thielen

University of Düsseldorf

Björn Jensen

University of Cologne

Rolf Kaiser Marc Oette Melanie Balduin Saleta Sierra Aragon Finja Schweizer Elena Knops Maria Neumann-Fraune Eugen Schülter Eva Heger Claudia Müller

University of Erlangen

Hauke Walter

Institut für Immunologie und Genetik Kaiserslautern

Martin Däumer