Le resistenze di HIV sono destinate a scomparire?

Andrea De Luca Istituto di Clinica delle Malattie Infettive

Univ. Cattolica S. Cuore, RomaMalattie 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)

0

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50

60

70

80

90

100

1999 2000 2001 2002 2003 2004 2005 2006

pe

rce

nt o

f p

atie

nts

Resistance NRTI

Resistance NNRTI

Resistance PI

Resistance any class

Multidrug resistance

Non B subtypes

n= 395 362 490 488 760 974 829 224Di Giambenedetto et al. Antivir Ther 2009

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10

20

30

40

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60

70

80

90

100

1999 2000 2001 2002 2003 2004 2005 2006

pe

rce

nt

of

pa

tien

ts

Resistance NRTI

Resistance NNRTI

Resistance PI

Resistance any class

Multidrug resistance

Non B subtypes

Di Giambenedetto et al. Antivir Ther 2009 n= 52 52 64 68 126 163 116 26

Prevalence of HIV resistance at several classes: in first line cART failures (ARCA db; n=717)

Di Giambenedetto S et al. EACS 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

00.10.20.30.40.50.60.70.8

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

NRTI NNRTI PI_major

n= 365 839 1620 2027 2199 2306 2591 2731 2581 1900 1274 325

Prevalence of different non-B subtypes by calendar year

0

2

4

6

8

10

12

199719981999200020012002200320042005200620072008

%

02_AG14_BG29_BFA1CDGOther

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

Di Giambenedetto S et al. EACS 2009

Prevalence of other NRTI-RMover calendar years

Di Giambenedetto S et al. EACS 2009

Prevalence of NNRTI-RMover calendar years

Prevalence of selected major PI-RMover 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

UK Collaborative Group on HIV Drug Resistance, AIDS 2007

All patients (n = 4454)

Acutely infected (n = 316)

NRTI

NNRTI

PI

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?

TDREDR

TDRTDR

EDR

Probabilità di trasmissione di DR:-efficienza intrinseca-carica virale-frequenza e modalità di esposizione

Probabilità di trasmissione di DR:>per mutazioni ad alta fitness>da pazienti off-therapy >in pazienti a diagnosi ignota>a pazienti non in terapia

Analisi filogenetica in pazienti naive (ARCA, prima sequenza, sottotipo B, n=442)

resistance class0

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any RNRTI RNNRTI R maj PI R

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ent r

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tant

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

EDR

TDR

noRPaziente 1

Paziente 2

Trattamento e fallimento

Potenziali cause di discordanza rispetto alle resistenze nei cluster

TDR

noRPaziente 1

Paziente 2

-Resistenze non rilevabili (quasispecie minoritarie)

-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