Le resistenze di HIV sono destinate a scomparire? Andrea De Luca Istituto di Clinica delle Malattie Infettive Univ. Cattolica S. Cuore, Roma Malattie Infettive Universitarie, AOU, Siena Background • I trattamenti più potenti e ad elevata barriera genetica dovrebbero determinare una riduzione delle resistenze • Sempre meno pazienti in trattamento presentano viremie rilevabili • Se la fonte primaria delle resistenze (i pazienti in fallimento con resistenze) viene ad esaurirsi, le resistenze dovrebbero scemare Prevalence of HIV resistance at several classes: all ARV-treated individuals (ARCA db; n=4,887) 100 90 80 percent of patients 70 Resistance NRTI 60 Resistance NNRTI 50 Resistance PI 40 Resistance any class Multidrug resistance 30 Non B subtypes 20 10 0 1999 2000 2001 2002 2003 2004 2005 2006 n= 395 362 490 488 760 974 829 224 Di Giambenedetto et al. Antivir Ther 2009 100 Prevalence of HIV resistance at several classes: in first line cART failures (ARCA db; n=717) 90 percent of patients 80 70 60 Resistance NRTI 50 Resistance NNRTI Resistance PI 40 Resistance any class 30 Multidrug resistance 20 Non B subtypes 10 0 1999 n= 52 2000 2001 2002 2003 2004 2005 2006 52 64 68 126 163 116 26 Di Giambenedetto et al. Antivir Ther 2009 Surveillance of the Epidemiology of Emergent HIV drug Resistance in Europe (SEHERE) (n=20763) Di Giambenedetto S et al. EACS 2009 Resistance to Drug Classes per Calendar Year 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 NRTI n= 365 839 1620 2027 2199 NNRTI 2306 PI_major 2591 2731 2581 1900 1274 325 Di Giambenedetto S et al. EACS 2009 Prevalence of different non-B subtypes by calendar year 12 02_AG 14_BG 29_BF A1 C D G Other 10 % 8 6 4 2 0 199719981999200020012002200320042005200620072008 Di Giambenedetto S et al. EACS 2009 Di Giambenedetto S et al. EACS 2009 Prevalence of type 1 TAMs over calendar years Di Giambenedetto S et al. EACS 2009 Prevalence of other NRTI-RM over calendar years Di Giambenedetto S et al. EACS 2009 Prevalence of NNRTI-RM over calendar years Di Giambenedetto S et al. EACS 2009 Prevalence of selected major PI-RM over calendar years Di Giambenedetto S et al. EACS 2009 Di Giambenedetto S et al. CROI 2010 Prevalence of transmitted HIV-1 drug resistance in Italy (n=1690) Bracciale L J Antimicrob Chemother 2009 Prevalence of transmitted HIV-1 drug resistance in Italy: subtype B only Bracciale L J Antimicrob Chemother 2009 Decline in transmitted HIV-1 drug resistance in the UK All patients (n = 4454) Acutely infected (n = 316) NRTI NNRTI PI UK Collaborative Group on HIV Drug Resistance, AIDS 2007 Resistance can be transmitted from one individual to another • Drug resistant virus is prevalent in primary infection and the transmission of resistant virus from individuals who have failed antiretroviral therapy is well documented – Reviewed by Tang JW & Pillay D. J Clin Virol 2004; 30:1–10 • Transmitted resistant virus persists for long periods of time – – – Pao D, et al. JAIDS 2004; 37:1570–1573 Little S, et al. Antirvir Ther 2003; 8:S129 Brenner B et al. AIDS. 2004; 18:1653–1660 • Resistant virus can be ‘re-transmitted’ from one treatment-naive individual to another – – Taylor S et al. AIDS Res & Hum Retroviruses 2003; 19:353–361 DeMendoza et al. Clin Inf Dis 2005; 41;1350–1354 ‘Transmission chains’ could generate an undetected epidemic of infection with drug resistant virus How much does transmitted DR depend from emerging DR? Probabilità di trasmissione di DR: -efficienza intrinseca -carica virale -frequenza e modalità di esposizione EDR EDR TDR TDR Probabilità di trasmissione di DR: >per mutazioni ad alta fitness >da pazienti off-therapy >in pazienti a diagnosi ignota >a pazienti non in terapia TDR Analisi filogenetica in pazienti naive (ARCA, prima sequenza, sottotipo B, n=442) 10 9 Percent resistant 8 7 6 any R 5 NRTI R 4 NNRTI R maj PI R 3 2 1 0 resistance class Assessment dei cluster tramite analisi filogenetica bayesiana Branch lengths expressed in nt substitutions per site Analisi filogenetica in pazienti naive (ARCA, prima sequenza, sottotipo B, n=442) • ML con 100 bootstrap runs: – 44 cluster identificati (mediana 2 pazienti: range 2-7) – 112/442 sequenze (25.3%) in cluster di naive – 7/44 cluster (15.9%) contengono resistenze – 13/41 (31.7%) sequenze con resistenze sono in cluster – 99/401 (24.7%) sequenze senza resistenze sono in cluster Cluster con sequenze discordanti riguardo le resistenze • 7 cluster con resistenze: – 3 concordanti (tutte le sequenze con resistenze) – 4 discordanti (alcune sequenze con resistenze, altre senza) Potenziali cause di discordanza rispetto alle resistenze nei cluster Trattamento e fallimento Paziente 1 noR Paziente 2 EDR TDR Potenziali cause di discordanza rispetto alle resistenze nei cluster Paziente 1 noR Paziente 2 -Resistenze non rilevabili (quasispecie minoritarie) TDR -Resistenze non trasmesse (minore fitness virale, bottleneck) Studi futuri per comprendere il fenomeno dei cluster discordanti • Ampliamento del campione • Dinamica temporale? • Studio della direzionalità delle trasmissioni • Necessità di studio di sequenze longitudinali De Luca A Curr Op HIV AIDS 2009, in press Conclusions: is HIV drug resistance disappearing? • Improved treatments and more active new agents are reducing EDR • There are reports of reduced TDR • Nonetheless, TDR is continuously fuelled by treatment naive individuals with at risk behaviors • The entity of TDR derived from treated and from naive patients requires clarification Conclusions: is HIV drug resistance disappearing? • Interventions towards naive individuals: – Earlier diagnosis – Behavioral changes – Treatment may significantly reduce TDR • In the future there might be a further reduction of EDR and TDR, but DR disappearance probably a dream: – EDR and TDR in low-middle income countries – Durability of current regimens • Will depend on wise and rationale usage/sequencing • No big news at the horizon Acknowledgements Acknowledgements • • • • • • • D Dunn, D Pillay, C Sabin UK-HIVDR and CHIC R Camacho, Lisbon M Ciccozzi, A Lo Presti, ISS, Roma, Italy P Sloot, Univ. of Amsterdam, the Netherlands ARCA: M Zazzi, C Balotta Euresist: ARCA, AREVIR (R Kaiser), Karolinska (A. Sonnerborg) Virolab: FP6 INFSO-IST-027446 (C Torti, D vd Vjver, AM Vandamme) • Computing Real-World Phenomena with Dynamically Changing Complex Networks (DYNANETS): FP7-233847 • Collaborative HIV and Anti-HIV Drug Resistance Network (CHAIN): FP7 HEALTH-2007-B -223131 Special acknowledgements Iuri Fanti, B.Eng.CS Mattia Prosperi, PhD