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AIDS CLINICAL ROUNDS

Genetic Differences in Vulnerability to Methamphetamine-related Brain Dysfunction: Implications for HIV

Mariana Cherner, PhD Associate Professor In Residence

Department of Psychiatry UCSD

AIDS Rounds 02/22/13

Objectives Overview of methamphetamine CNS effects

Results of neuropsychological study of methamphetamine

Preliminary evidence for individual differences in methamphetamine-associated neurocognitive impairment: role of metabolism

Relevance for HIV and HCV

Future study

Methamphetamine

Methamphetamine-related brain injury

Damage / loss of dopamine terminals and transporters Oxidative stress Mitochondrial injury Glutamatergic excitotoxic injury Glial activation Increase in inflammatory cytokines/chemokines Cerebrovascular pathology Brain metabolic and perfusion changes Loss of neurons in subcortical regions

Meta-analysis: Neuropsychological Effects of Methamphetamine

Scott, et al. (2007). Neuropsychol Rev 17:275–297

Problems with published studies Studies of methamphetamine users show

cognitive and psychomotor abnormalities, but

» Few documented HIV serostatus

» None documented HCV serostatus

» Control Ss typically have less other substance abuse

» Control Ss often have better premorbid functioning

Cognitive impairment in subjects with single or co-morbid HIV and Meth Risks

(N=398)

Rippeth et al, JINS, 2004

Rates of NP Impairment by Number of Meth, HIV, and HCV Risk Factors

Predictors of NP Impairment among

Methamphetamine Dependent Individuals

Objectives 1. Determine prevalence of NP impairment in HIV-, HCV-

meth users compared to healthy controls with similar demographic characteristics and estimated premorbid functioning.

2. Determine which meth use characteristics are associated with NP impairment.

Greater proportion NP impairment among methamphetamine dependent

based on age, education, sex, and ethnicity adjusted test scores

Confounding Variables did not Predict Global Neuropsychological Impairment

alcohol whole model test: X2 = 6.99 p = .03 alcohol effect: X2 = 1.56 p = .21 meth effect: X2 = 6.37 p = .01 cocaine whole model test: X2 = 5.60 p = .06 cocaine effect: X2 = 0.21 p = .62 meth effect: X2 = 5.28 p = .02

cannabis whole model test: X2 = 5.80 p = .05 cannabis effect: X2 = .44 p = .50 meth effect: X2 = 5.4 p = .02 MDD lifetime: X2 = .06 p = .81 MDD current: X2 = .45 p = .50 ASPD: X2= .04 p = .85 ADHD: X2 = .02 p = .89

Participant background characteristics did not explain NP impairment among meth addicts

Mean (sd) or Proportion NP Impaired NP Normal p value

Age 34.7 (8.8) 39.1 (10.8) NS

Education 12.2 (1.5) 12.9 (2.0) NS

n (%) Male 16 (73%) 24 (75%) NS

n (%) Non-White 4 (18%) 8 (25%) NS

WRAT-3 Reading Quotient 95.6 (10.1) 102.7 (9.0) .001

n (%) Lifetime Alcohol Dependence 5 (23%) 14 (44%) NS

n (%) Lifetime Cannabis Dependence 4 (18%) 7 (22%) NS

n (%) Lifetime Cocaine Dependence 3 (14%) 6 (19%) NS

n (%) Current Major Depression 2 (9%) 2 (6%) NS

n (%) Antisocial Personality Disorder 6 (27%) 8 (25%) NS

n (%) Attention Deficit / Hyperactivity 2 (10%) 3 (10%) NS

Methamphetamine use characteristics did not predict neuropsychological impairment

Mean (sd) or Proportion NP Impaired (n=22) NP Normal (n=32) p value

Length of abstinence in days 125.2 (96.1) 131.1 (103.2) NS

Years of use 11.8 (5.2) 12.1 (5.4) NS

Lifetime grams consumed 4234 (3421) 4415 (4603) NS

Average grams per year of use 413 (395) 363 (340) NS

n (%) using in the last 30 days 3 (14%) 4 (13%) NS

Primary mode of METH use NS

n (%) ingest 0 1 (3%)

n (%) inject 2 (9%) 5 (16%)

n (%) insufflate 7 (32%) 12 (37%)

n (%) smoke 13 (59%) 14 (44%)

Cherner, et al. (2002). JINS

Conclusions from previous findings:

The lack of correspondence between methamphetamine exposure and NP

impairment suggests individual differences in vulnerability to

methamphetamine neurotoxicity

Possible targets to investigate

Pharmacodynamic » Metabolic pathways/enzymes » Receptors » Transporters » Intracellular signaling

pathways » DNA binding proteins

Pharmacokinetic » Metabolism » Distribution » Absorption

NP impairment could be related to » Genes associated with

drug response » Genes associated with

neuropathologic vulnerability

» Their combination

From: K. Heinzerling, 2007

Cytochrome P450, family 2, subfamily D, polypeptide 6

Enzyme that metabolizes many psychoative compounds

Highly polymorphic

Located on chromosome 22q13.1

Detectable in brain

Responsible for primary oxidative metabolism of methamphetamine

Source: http://en.wikipedia.org/wiki/Image:CYP2D6_structure.png. Borislav Mitev

Source: Lin et al, 1997

CYP2D6 Metabolic Activity

Over 80 known genetic polymorphisms, many affecting metabolic rate

Resulting phenotypes (%= prevalence in Caucasians): » Extensive metabolizer is wildtype - 65 to 70%

» Intermediate metabolizer - 20-30%

» Poor metabolizer - 5 to 14%

» Ultra-rapid metabolizer - rare

CYP2D6 genetic mutations and resulting metabolic activity

Poor Metabolizers Have no active CYP2D6 alleles (or 1 partially active)

Are at greater risk of drug-induced side effects due to diminished drug elimination

most common mutant alleles in Caucasians: CYP2D6*3, CYP2D6*4, CYP2D6*5, CYP2D6*6, which account for 93-97% of the PM phenotypes

Low prevalence among Asians, higher in Africans Gonzalez et al, 1988; Gough et al, 1990; Kimura et al, 1989; Marez et al, 1997

Study Objective and Hypothesis

Primary Aim: To determine the influence of CYP2D6 phenotype corresponding to high or low metabolic activity on cognitive functioning in abstinent methamphetamine addicts

Hypothesis: Extensive metabolizers will clear methamphetamine more effectively and therefore will show better NP functioning

RESULTS: Methamphetamine Use Characteristics by Metabolic Phenotype

Mean (SD) or % Extensive (n=33) Intermediate (n=17) Poor (n=3)

Age Onset 22 (9) 24 (6) 18 (2)

Total Years of Use 12 (5) 12 (5) 11 (9)

Days Abstinent 131 (107) 113 (80) 117 (63)

METH Density (g/yr) 413 (324) 370 (448) 240 (279)

Lifetime grams 4719 (4237) 3559 (3787) 4267 (5869)

Last Year grams 347 (314) 267 (304) 333 (422)

Binge use predominant 4% 5% 0%

Route* injection intranasal smoke

6% 29% 65%

25% 50% 25%

33% (1/3) 33% (1/3) 33% (1/3)

*p < .05

Extensive Metabolizers show worse Neuropsychological Performance

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

GDS* Proc Speed Wk Memory Verbal Learning* Recall* Executive† Motor

Def

icit S

core

Extensive (n=33)Intermediate/Poor (n=20)

*p<.05, † p<.10

worse

better

Greater Prevalence of Neuropsychological Impairment in Extensive Metabolizers

*p<.05 †p<.10

CYP2D6 Rank Ordered Activity

Activity Phenotype n

1 PM 3

2 IM 4

3 IM 13

4 EM 14

5 EM 18

Metabolic activity rank:

1: two non-functional alleles

2: one decreased function and one non-functional allele

3: one normal function and one non-functional allele, or two decreased function alleles

4: one normal function and one decreased function allele

5: two normal function alleles

(based on activity data by Zanger, 2004)

Correlations between NP Deficit Scores and CYP2D6 metabolic activity based on

combination of alleles Deficit Score Spearman Ρ Prob>|ρ|

Global 0.36 0.009

Processing Speed 0.31 0.025

Attn/Wk Memory 0.16 0.257

Verbal Fluency 0.18 0.192

Learning 0.39 0.004

Delayed Recall 0.20 0.156

Executive 0.35 0.010

Motor 0.02 0.868

Linear or Quadratic?

Summary Methamphetamine use parameters are poor predictors of cognitive

impairment, suggesting individual differences in vulnerability to neurotoxic effects

Results of CYP2D6 study implicate the products of methamphetamine metabolism as a source of brain injury

High CYP2D6 metabolic activity is associated with worse neurocognitive outcomes, possibly as a result of greater formation of harmful methamphetamine oxidative metabolites

Low metabolic activity may also be associated with worse neurocognitive outcome, perhaps as a result of lower clearance of the parent compound

Other factors may be at play: cannabis? antidepressants? meth dose?

Summary (cont) First study to suggest that differential meth metabolism is

associated with neurocognitive outcomes

Findings are supported by in vitro studies showing greater cellular toxicity of methamphetamine1 and other substituted amphetamines (MDMA, MTA)2 under conditions of normal vs. low CYP2D6 activity

Recent study of MDMA users showed ultra-rapids had worse cognitive performance

Reasons to pursue the CYP2D6 story

The case for toxic metabolites Hydroxy metabolites of amphetamine and meth are

neurochemically and behaviorally active

have a much longer half-life than the parent compound (e.g., 1.5 days compared 45 minutes, in rat brain)

can induce release and inhibit uptake of norepinephrine and dopamine with almost equivalent potency to the unmetabolized substances.

are taken up by dopaminergic terminals

accumulate in striatum and hypothalamus after chronic administration.

can induce hyperlocomotion and stereotyped behaviors

in vitro evidence

Meth hydroxy metabolite causes greater cytotoxicity than unmetabolized meth

Cells expressing the active form of CYP2D6 show greater MDMA toxicity than cells with less active forms

toxicity of MDMA metabolite (N-methyl-α-methyldopamine) was found 100-fold stronger than unmetabolized MDMA

Brain CYP2D6 Activity

CYP2D6 protein and mRNA are detectable in a number of brain regions, both in neurons and glia

CYP2D6 metabolic activity is detectable in animal brain microsomes

CYP2D6 involved in biotransformation of dopamine and serotonin from trace amines in brain – thought to influence DA – 5HT balance.

Infectious Disease Rationale CYP2D6 activity is dysregulated in HCV and HIV

there are no studies investigating whether the meth-associated brain dysfunction observed in HIV is mitigated by genotypes influencing meth metabolism.

it is not known how the bioavailability of meth or its metabolic products may contribute to the added neuropsychological sequelae that meth abuse confers in the context of HIV and HCV

Ritonavir and SSRI’s (and other Rx) commonly prescribed are CYP2D6 inhibitors

CYP2D6 Genotype and Cognitive Deficits in

Methamphetamine Users with/without HIV

new R01 study to begin April 2013

Aim 1

To determine effects of CYPD6 genotype on methamphetamine-associated neurocognitive deficits.

Address whether CYP2D6 genotype contributes to differences in frequency or severity of neurocognitive deficits among meth users, and explore alternative explanations of the data, including whether CYP2D6 genotype explains differences in meth consumption or mode of use.

Aim 2

To identify factors that mitigate CYP2D6 effects on methamphetamine-associated deficits.

Whether the relationship between CYP2D6 genotype and cognitive outcomes is altered in the context of HIV or HCV infection, and how concomitant use of substances that interact with CYP2D6 (e.g., ritonavir, antidepressants) affects that relationship.

Exploratory Aim

To explore effects of interactions between CYP2D6 and other genes on neurocognitive outcomes.

Given the role of CYP2D6 in biotransformation of dopamine and serotonin, focus on common polymorphisms that code for dopamine and serotonin activity/availability.

Catechol-o-methyl transferase COMT Val158Met Dopamine transporter Dopamine receptors 2, 3, 4 Tryptophan Hydroxylase 2 DOPA/tryptophan decarboxylase Monoamine oxidase A Serotonin receptor 2A Serotonin-transporter Serotonin-transporter-gene-linked polymorphic region

Current & Future Directions

Determine degree of brain injury as a function of CYP2D6 phenotype in autopsy tissues from deceased methamphetamine users (R03 funded study)

Measure actual relationship between meth metabolites cellular injury in an in vitro model with mixed brain cell cultures (submitted R21 application)

Public Health Importance

Tools for providers to make advantageous treatment choices. » e.g., if findings are replicated: prescribe a CYP2D6 inhibitor to turn a

genotypically extensive metabolizer into a poor metabolizer.

» if findings are reversed: avoid further inhibiting CYP2D6 activity by choosing alternate medications.

» educate patients/clients about particularly risky practices (e.g., what are the risks of using meth while being on a ritonavir-containing cART regimen?

Acknowledgments NIDA grants (R03-DA27513; (P01-DA12065)

NIMH (P30-MH62512)

HNRP Study Participants • HNRP Staff & Collaborators