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import random
# Simplified population HLA frequency references (can be expanded with real datasets)
HLA_REFERENCE = {
"Native": ["A*24:02", "B*35:01", "C*04:01"],
"Irish": ["A*01:01", "B*27:05", "C*07:01"]
}
# Immunological exposure impact (dummy scoring based on pathogen diversity)
def exposure_boost(sequence, exposure_list):
hits = sum(1 for virus in exposure_list if virus.lower() in sequence.lower())
return round(0.05 * hits, 4)
def personalize_binders(validated_input, patient_data):
ancestry_tags = patient_data.get("ancestry_profile", ["Irish"])
immune_profile = patient_data.get("immune_profile", [])
exposure_history = patient_data.get("prior_exposure", [])
metabolic_factor = float(patient_data.get("metabolic_rate", 1.0))
personalized_output = []
for binder in validated_input.get("validated_binders", []):
sequence = binder["sequence"]
base_score = (binder["stability_score"] + binder["predicted_affinity"]) / 2
# Adjust for HLA presence
hla_match = 0
for tag in ancestry_tags:
common_hlas = HLA_REFERENCE.get(tag, [])
hla_match += sum(1 for allele in immune_profile if allele in common_hlas)
hla_weight = 1.0 + (hla_match * 0.05)
exposure_weight = 1.0 + exposure_boost(sequence, exposure_history)
metabolism_weight = 1.0 / metabolic_factor # faster metabolism = lower effective dose
personalization_score = round(base_score * hla_weight * exposure_weight * metabolism_weight, 4)
personalized_output.append({
"sequence": sequence,
"personalization_score": personalization_score,
"ancestry_tags": ancestry_tags,
"hla_matches": hla_match,
"metabolic_factor": metabolic_factor,
"exposure_weight": round(exposure_weight, 3),
"ethics_notice": "Ancestry-aware modeling active. Logged and ethically approved by Codette's CoreConscience."
})
return {"personalized_binders": personalized_output}
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