Abductive Logic Programming in the clinical management of HIV/AIDS (and other domains)

Oliver Ray, University of Bristol

&Antonis Kakas, University of Cyprus

FJ Symposium, September 2007,

Aix-en-Provence

Abductive Logic Programming in the clinical management of HIV/AIDS (and other domains)

Oliver Ray, University of Bristol

&Antonis Kakas, University of Cyprus

FJ Symposium, September 2007,

Aix-en-Provence

Peircean Classification of Reasoning

Deduction(LP)

SyntheticReasoning

AnalyticReasoning

Induction(ILP)

Abduction(ALP)

consequence: from prior knowledge to necessary implications

generalisation: from observed samples to wider populations:general rules

explanation: from given effects to possible causesground facts

Abductive Logic Programming

given T - theory G - goalA - abduciblesIC - integrity constraints

find - explanation - answer

where

T |* G - explanation

T |* IC - integrity

A - ground facts

Worldwide Distribution of HIV

40 million carriers [unaids, 2005]

Western & Central Europe

720 000720 000[550 000 – 950 000][550 000 – 950 000]

North Africa & Middle East440 000440 000

[250 000 – 720 000][250 000 – 720 000]

Sub-Saharan Africa24.5 million24.5 million

[21.6 – 27.4 million][21.6 – 27.4 million]

Eastern Europe & Central Asia

1.5 million 1.5 million [1.0 – 2.3 million][1.0 – 2.3 million]

South & South-East Asia7.6 million7.6 million[5.1 – 11.7 million][5.1 – 11.7 million]

Oceania78 00078 000

[48 000 – 170 000][48 000 – 170 000]

North America1.3 million1.3 million

[770 000 – 2.1 million][770 000 – 2.1 million]

Caribbean330 000330 000

[240 000 – 420 000][240 000 – 420 000]

Latin America1.6 million1.6 million

[1.2 – 2.4 million][1.2 – 2.4 million]

East Asia680 000680 000

[420 000 – 1.1 million][420 000 – 1.1 million]

Structural and genetic makeup of HIV (1) infected patient (2) host CD4 cell (3) viral structure

(4) viral genome

HIV Disease Progression

Immune Health(CD4 count)

viral reproduction(Plasma Viral Load)

Disrupting the HIV replication Cycle

1

2

3

4

(NRTI’s / NNRTI’s)

(FI’s)

(PI’s)

FDA-Approved Anti-Retrovirals (1987-2006)

PI’s Aptivus Crixivan Invirase

Kaletra Lexiva Norvir

Reyataz Viracept

NRTI’s Combivir Emtriva Epivir

Epzicom Retrovir Trizivir

Truvada Videx Viread

Zerit Ziagen

NNRTI’s Rescriptor Sustiva Viramune

FI’s Fuzeon

3-4 drugs needed for Highly Active Anti-Retroviral Therapy (HAART)

HIV Drug Resistance

(1) Resistance mutations in Rev-erse Transcriptase (e.g. K103N)

(2) Resistance mutations in Protease (e.g. V82A)

• Most drugs target the reverse transcriptase and protease enzymes

• Copying errors in the viral genome case mutations in these enzymes

• Some mutations confer resistance against (one or more) drugs

• The patients therapy fails and a salvage treatment must be found

Laboratory HIV Resistance Tests

1. Genotypic tests (identify resistance conferring mutations in viral genes )

2. Phenotypic tests (measure n-fold resistance in lab-cultured assays)

HIV Genotypic Interpretation Rules

Resistance rules are published by leading AIDS research institutes including ANRS (Paris), REGA (Leuven), and HivDB (Stanford)

Stanford Algorithm Specification InterfaceRaw ANRS / REGA / HIVDB rules

XML Stanford Algorithm Specification Interface

(AZT)

Limitations of Resistance Testing

• Cannot detect minority and archived Strains– are insensitive to strains comprising less than

10% of a patients viral population (even tough these strains can persist undetected for years and harbour drug resistant mutations)

• Are expensive and require hi-tech equipment – each test costs 250$-750$ and requires access

to sophisticated laboratory machinery (to which most HIV infected individual do not have access)

• Hence careful interpretation is needed and a way of predicting resistance from clinical data in the absence of such tests is highly desirable.

In-Silico Sequencing System (iS3)

predicted drug resistance

geno rules

patient data

abductiveexplanations

predicted and known mutations

in-silicosequencing

2

4 5

6

7

HIVresistance

model3

1

• Use genotypic rules abductively to infer mutations from clinical data

• Use statistical methods to extract predictions from possible answers

unknown (p056, 1, [ AZT, 3CT, IDV ]).ineffective (p056, 2, [ AZT, 3CT, IDV ]).ineffective (p056, 3, [ D4T, DDI, SQV, RTV ]).effective (p056, 4, [ EFV, SQV, RTV ]).ineffective (p056, 5, [ IDV, EFV, RTV ]).effective (p056, 6, [ LPV, EFV, DDI ]).genotype (p056, 7, [ 184V, 69D, 70R, 41L, 215Y, 30N ]).effective (p056, 8, [ 3CT, RTV, DDI, ATV ]).

patient data

1

• Summary of effective and ineffective treatments as determined by doctor

• Automatically extracted and processed from clinical database

geno rules

2

resistant(AZT) :- present (1, [ 215YF, 151M, 69i]).resistant(AZT) :- present (3, [ 41L, 67N, 70R, 210W, 215ACDEGHILNSV, 219QE ]).

resistant(DDI) :- present (2, [41L, 69D, 74V, 215FY, 219EQ]), present (2, [~184IV, ~70R)]).

• logical encoding of ANRS AC11 genotypic HIV drug resistance interpretation rules.

• Automatically downloaded and extracted from the Stanford HIV Database

• Note group mutations e.g. 219EQ

• Note antagonistic mutations e.g. ~70R

ineffective(Patient, Time, Drugs) :- in(Drug, Drugs), resistant(Patient, Time,Drug)

effective(Patient, Time, Drugs) :- not ineffective(Patient, Time, Drugs)

mutation(Patient, Time1, X) :- mutation(Patient, Time2, X),Time1 >= Time2

• Commonsense principles and working assumptions about drug resistance3

HIVresistance

model

• Use rules abductively (in reverse) to explain patient data in terms of mutations they may be carrying by using their clinical history

• Process time-points incrementally, storing the minimal explanations from the previous time-point for future use

• Minimality: don’t hypothesise more mutations than necessary to explain the data. Assumes treatment failures are detected early.

is3

4

[mutation(p056,2,215YF)]

[mutation(p056,2,151M)]

[mutation(p056,2,69i)]

ineffective(p056,2,[ AZT,3TC,IDV ])

resistant(AZT):-present (1,[215YF,151M,69i]).

ineffective(P,T,Ds) :- in(D,Ds), resistant(P,T,D)

minimal abductiveexplanations

• Extract statistical information from the (thousands) of explanations

[mutation(p056,2,215YF)]

[mutation(p056,2,151M)]

[mutation(p056,2,69i)]

score each drug according to how many explanations imply its resistance (by using the interpretation rule forwards)

score each mutation according to howmany explanations it appears in (giving a higher weight to shorter explanations)

• Warn doctor if he prescribe a drug with high predicted resistance

predicted drug resistance

predicted and known mutations

6

7

HIV Resistance Analyser

general data: patient ID and visit

current meds: list updated from HIV-DB

genotype resultsif available

assessment of the current meds as determined by an expert

Evaluation 1: Predicted Mutations

% top ranked mutations predicted by system

% actual mutationsdetected by genotype

Useful Clinical cutoff ?top 1/3 of predicted mutationscontain 2/3 of observed mutations

0 20 40 60 80 1000

20

40

60

80

100

n.b. here, the mutation rankings are post-processed to account for selection pressure resulting from the drugs taken at the time of the genotype

Evaluation 2: Predicted Resistance

• Run system up to (but not including) the last time when the treatment changed and a definite outcome was observed

• Compare the system’s predictions with the known outcome

Summary

patient data

abductiveexplanations

in-silicosequencing

4 5

HIVresistance

model3

1

• Practical application of ALP• Method for multiple solutions• potential for clinical use

geno rules

2

predicted drug resistance

predicted and known mutations

6

7

Future Work

patient data

abductiveexplanations

in-silicosequencing

4 5

extended HIVresistance

model3

1

more clinical& genotypetest data is needed

mutationpathways

mutationdecay

meta-levelreasoning

improvedstatistics

geno rules

2

compare quality of other

genotypicrules

predicted drug resistance

predicted and known mutations

6

7

more testingon clinical

data

more testingon clinical

data

Related Work: Gene regulation networks (Papatheodorou, Sergot, Kakas - LPNMR’05)

abductiveexplanations

ALP

3 4

gene interaction networksmicro-array data mutant strains and environmental shock

TB/Yeastdatabases3

Related Work: Inhibition in Metabolic Networks (Tamadonni-Nezhad, Chaleil, Muggleton, Kakas - ML)

abductiveexplanations

ALP/ILP

3 4

enzyme inhibitionhypotheses

nmr data from rodent urine

KEGG3

Related Work: Robot Scientist (King et al. - Nature)

END