abductive logic programming in the clinical management of hiv/aids (and other domains) oliver ray,...
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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