1. Mattia CF Prosperi, PhD ahnven@yahoo.it Clinic of Infectious Diseases Catholic University of Sacred Heart Largo F. Vito, 1 – 00168 - Rome, Italy

2.  HIV-1 biology, treatment and resistance  Epidemiology  Surveillance of resistance trends  Phylogenetics, HIV-1 evolution  Clustering of HIV-1 mutations  Intra-host analysis  HIV-1 replication, natural genetic drift and selective drug pressure  Differential equation modelling  Optimising treatments with machine learning  Prediction of HIV-1 co-receptor usage  Prediction of in vivo HIV-1 virologic response to treatments  Genotype-based models  Treatment history-based models  Perspectives  Modelling time to viral rebound, and resistance emergence  Modelling epidemic with complex networks

4.  Usually focus on specific problems  Incidence of new infections  Temporal trends of treatment efficacy  Determinants of virologic or immunologic failure  …  Standard models  Univariable analysis (chi-square, t-test, rank-sum)  Linear, logistic regression  Survival analysis (Kaplan-Meier, Cox proportional hazard)  Often limited when considering predictive ability of the models  Complex Network models  HIV-1 is peculiar!  not only sexually transmitted, long asymptomatic stage, high rate of evolution, integration into host genome, no natural eradication, rapid development of drug resistance…  SIR-like models, adjusted for MANY other factors like the drug resistance emergence (Smith, Blower et al., Science, 2010)

5.  Several epidemiological studies carried out at our institute  Temporal trends of drug resistance in Europe, considering different inhibition classes  We assessed the temporal trends of resistance by fitting a linear model, adjusting for potential confounders such as age, gender, mode of HIV transmission, introduction of new drugs…

6.  Several scales of analysis  Subtype evolution  Transmission events (and drug resistance transmission)  Not always the phylogenetic reconstruction is able to trace the epidemiological evidence, due to sparse sampling (intra-host evolution does not proceed at a constant rate) Note: phylogenetic analysis constructs a hierachy of sequences that represents the evolution from an hypothetical ancestor. Several techniques are available, from distance-based clustering to maximum likelihood, to bayesian clustering

7.  Probability of DR transmission  intrinsic efficiency  viral load  frequency and modality of exposition

8.  ≈12,000 group M subtype B HIV-1 polymerase sequences collected from Italian ARCA DB  Maximum-likelihood parallel phylogeny, computationally intensive  There is no methodology for automatic cluster identification  New technique for partitioning a phylogenetic tree  Depth first visit with constraints on node reliability and intra/inter-cluster patristic distance distributions  Validated on a set of known transmission events

9. 11,541 sequences 9,855 patients Tree rooted on outgroup subtype J (ancient differentiation) 3D-hyperbolic geometry view Fractal dimension ≈1.6 Sustained level of differentiation

10. A more recent calendar year of sequencing Patients from central-Italy vs those from northern- or southern-Italy Heterosexuals and homosexuals vs injecting drug users Younger patients Patients with more recent infections ( =14 years) Presence of resistance mutations in the protease gene

11.  Similar to phylogenetic analysis, but performed on transposed sequence alignments  Useful to find associations among mutations under particular drug pressures  Basis for structural analysis (Prosperi et al, ARHR 2009)

12.  Description of patient’s  viral dynamics  immune response  Suitable for control theory (if equations could be treated analytically)  Difficulties in dealing with prediction of therapy outcomes (see the constant η values, indeed they should change!)  Difficulties modelling resistance outbreak (stochasticity, multi-strain models) System of differential equations

13.  New approach to account both for treatment administration and viral evolution  Stochastic model for viral natural evolution in absence of treatment  Calculation of instant resistance at different time points using in-vitro known drug susceptibility  Usage of time-varying resistance [] in the differential equations and approximation with numerical solutions  Usage of time-varying resistance [η= η(t)] in the differential equations and approximation with numerical solutions  Calculation of number of virions in the next replication  Selection of resistant strains with roulette wheel procedure (from genetic algorithm theory)

14.  Different combination treatments evaluated, along with therapy sequencing policies  Although theoretically complex and sound, the model was not suitable for clinical practice (Prosperi et al, Bioinformatics 2008)

15.  We do not attempt to define an explicit model  Extensive use of machine learning  Linear and non-linear models  Feature selection  Robust validation  In-vitro: prediction of HIV-1 co-receptor usage  In-vivo: prediction of virological response to combination antiretroviral therapy (cART)  With viral genotypic information  Without viral genotypic information  Designed for low/middle income countries

16.  HIV-1 can use two different co-receptors (CCR5/CXCR4)  Entry inhibitors block only the CCR5 co- receptor  The model helps to decide if a patient can be given an entry inhibitor or not, given his viral sequence  Analysis using whole envelope region and other patient’s characteristics  Logistic regression is a suitable model  Not inferior to complex non-linear models  Performance (with robust validation) up to  93% accuracy  0.77 sensitivity  0.93 AUC (Prosperi et al, ARHR 2009)

17.  Predicting the actual viral load changes following treatment switches is a challenging task  Individual variability of immune response to infections add noise to the system  Large number of possible therapeutic combinations leads to complex viral evolutionary pathways  Other treatment-related factors such as pharmacokinetics and patient adherence to therapy play a crucial role in the control of virus replication and the development of resistance  We focused on fixed patient’s follow up times (n-weeks of therapy)

18.  EuResist is a no-profit foundation (formerly EU-funded project), a consortium were hospitals, biology labs and universities cooperate  Karolinska institute, University of Siena, University of Cologne, Max Planck Institute, IBM…  It is the largest data base in the world comprising clinical, demographic and genomic data of HIV+ patients from national cohorts of Western Europe (at now Belgium, Italy, Germany, Sweden, Spain, Luxembourg)  ≈34’000 patients  ≈500’000 CD4 and ≈400’000 HIV-RNA measurements  ≈100’000 antiretroviral therapies  ≈31’000 HIV sequences (polymerase)  Open to any kind of collaboration and data exchange

19.  Treatment Change Episodes (TCE) with a new cART  Baseline HIV RNA load, CD4+ T cell counts  Baseline HIV polymerase genotype and subtype  Patient’s demographics (age, gender, ethnicity, mode of HIV transmission…)  Previous drug usages (>1 year usage) for each drug class and each single drug  8-weeks and 24-weeks HIV RNA response  Success defined as the achievement of 2 Log decrease from baseline at 8-weeks)

20. The EuResist repository was queried and generated more than 3,000 TCE that were used for training and validating a prediction engine

21.  Statistical learning models  Logistic Regression with higher-order interactions (LR)  AIC stepwise selection  Random Forests (RF)  Feature importance evaluation with Strobl’s method  Bayesian networks (BN)  Three independent models were merged improving performance  Extra sample error estimation  Multiple ten-fold cross validation (MCV)  Adjusted t-test on MCV performance distributions for model comparison  External independent test set evaluation  Comparison against human experts  Comparison against rule-based algorithms (Stanford, Rega, ANRS)

22. Statistical models Web-service Customised cART sequencing Patient’s Age, gender HIV RNA CD4 Experienced drugs HIV genotype

23.  The EuResist model outperforms the whole set of state-of-the-art techniques (i.e. rule-bases) and is as good as the world’s best human experts  The average accuracy on validation is 76%, and AUC is 0.77 (Prosperi et al, Antivir Ther 2009)

24.  In high-income countries, guidelines recommend genotypic resistance testing (GRT) both before starting antiretroviral therapy (ART) and at ART failure  Appropriate funding and/or facilities to perform GRTs may be not available in low-middle income countries (LMIC), leaving physicians to switch therapy based solely on the clinical/immunological conditions (sometimes even without virological monitoring)  Treatment history (TH) is one of the most crucial factors to play a role in the response to a new treatment.  Other important factors are virologic and immunologic monitoring

25.  GRT-based vs TH-based models were compared to see if there were sensible loss in performance  Performance of the model were tested in extra-EU-like scenarios  Tests on a larger set of TCE without the mandatory GRT baseline attribute were carried out No statistically significant differences found by comparing GRT and TH models

26.  We want to design and test a model that predicts viral load rebound over time using  Patient’s viral genotypic information  Patient’s clinical and demographic background  Suitable models: Cox regression, random survival forests  Need to define an appropriate goodness of fit  Preliminary inquire on the EuResist DB gave a considerable number of training instances

27.  Also, we might be interested in a model that predicts drug resistance emergence

28.  Design and test an epidemic model for HIV-1 using complex networks  Start from Science paper and from other models presented in literature  New insights  Capability to handle dynamics at a regional, national and international level  Effective description of  Infection incidence over different risk group strata  Homogeneous vs heterogeneous mixing?  Drug resistance trends  Prevision of trends with the introduction of new inhibition classes  Prevision of HIV-1 evolution with respect to drug resistance prevalence in the treatment-naive population  Account for transmitted drug resistance from treatment-naive and treatment-experienced patients  How much shall we go into details as concerns the intra/inter-host genetic HIV-1 evolution?