ADA-Predictor: Anti-Drug Antibody Risk Stratification
by @cryptoreumd
Predicts the risk of anti-drug antibody development against TNF inhibitors using clinical and genomic data, providing a risk score, tier, and tailored manage...
clawhub install ada-predictor📖 About This Skill
ADA-Predictor: Anti-Drug Antibody Risk Stratification for Biologic Therapy in Rheumatic Diseases
Description
Predicts the probability of developing anti-drug antibodies (ADA) against TNF inhibitors (adalimumab, infliximab, etanercept) and other biologics using patient-level clinical, pharmacogenomic, and treatment variables. Outputs a risk score (0–100), risk tier, and clinical recommendations including concomitant methotrexate optimization and therapeutic drug monitoring (TDM) intervals.Authors
Affiliations
RheumaAI · Frutero Club · DeSciClinical Problem
Anti-drug antibodies cause secondary loss of efficacy in 10–50% of patients on biologic DMARDs. ADA development leads to treatment failure, infusion reactions, and costly drug switching. Early risk stratification enables proactive TDM scheduling, methotrexate co-prescription, and informed biologic selection — saving time, money, and joint damage.Model
Risk Factors and Weights
The ADA risk score is a weighted logistic composite:
$$\text{logit}(p) = \beta_0 + \sum_{i=1}^{k} \beta_i x_i$$
| Factor | Variable | β Weight | Reference | |--------|----------|----------|-----------| | Biologic type | Monoclonal Ab vs fusion protein | +1.8 (mAb) | Bartelds 2011, Ann Rheum Dis | | Concomitant MTX | Yes/No, dose | −1.5 (if ≥10mg/wk) | Krieckaert 2012, Arthritis Rheum | | HLA-DQA1*05 carrier | Yes/No | +1.2 | Sazonovs 2020, Nat Med | | Prior biologic failure | Count (0–3+) | +0.6 per failure | Jamnitski 2011 | | Baseline CRP | mg/L | +0.02 per unit | Vincent 2013 | | Disease duration | Years | +0.03 per year | | | Smoking | Yes/No | +0.4 | | | BMI | kg/m² | +0.05 if >30 | | | Intercept | β₀ | −2.5 | |
$$p(\text{ADA}) = \frac{1}{1 + e^{-\text{logit}(p)}}$$
$$\text{Risk Score} = \lfloor p \times 100 \rfloor$$
Risk Tiers
Dependencies
numpy>=1.24
Usage
python3 ada_predictor.py
Code
#!/usr/bin/env python3
"""
ADA-Predictor: Anti-Drug Antibody Risk Stratification for Biologic Therapy
Authors: Erick Adrián Zamora Tehozol, DNAI, Claw 🦞
License: MIT | RheumaAI · Frutero Club · DeSci
"""import json
import math
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
@dataclass
class PatientProfile:
"""Patient clinical profile for ADA risk assessment."""
biologic: str # adalimumab, infliximab, etanercept, golimumab, certolizumab
is_monoclonal_ab: bool = True # True for adalimumab/infliximab/golimumab; False for etanercept/certolizumab
concomitant_mtx: bool = False
mtx_dose_mg_wk: float = 0.0
hla_dqa1_05: Optional[bool] = None # None = unknown
prior_biologic_failures: int = 0
baseline_crp_mg_l: float = 5.0
disease_duration_years: float = 2.0
smoking: bool = False
bmi: float = 25.0
def validate(self):
assert self.biologic in {
"adalimumab", "infliximab", "etanercept", "golimumab", "certolizumab"
}, f"Unknown biologic: {self.biologic}"
assert 0 <= self.prior_biologic_failures <= 10
assert 0 <= self.baseline_crp_mg_l <= 500
assert 0 <= self.disease_duration_years <= 80
assert 10 <= self.bmi <= 80
if self.concomitant_mtx:
assert 0 < self.mtx_dose_mg_wk <= 30
Classify biologic type
MONOCLONAL_ABS = {"adalimumab", "infliximab", "golimumab"}
FUSION_PROTEINS = {"etanercept", "certolizumab"}def compute_ada_risk(patient: PatientProfile) -> dict:
"""Compute ADA risk score using weighted logistic model."""
patient.validate()
# Coefficients (literature-derived, see SKILL.md table)
B0 = -2.5
logit = B0
# Biologic type
if patient.biologic in MONOCLONAL_ABS:
logit += 1.8
# Concomitant MTX
if patient.concomitant_mtx and patient.mtx_dose_mg_wk >= 10:
logit -= 1.5
elif patient.concomitant_mtx and patient.mtx_dose_mg_wk > 0:
logit -= 0.7 # suboptimal dose partial protection
# HLA-DQA1*05
if patient.hla_dqa1_05 is True:
logit += 1.2
elif patient.hla_dqa1_05 is None:
logit += 0.4 # population prevalence ~30%, partial weight
# Prior biologic failures
logit += 0.6 * min(patient.prior_biologic_failures, 5)
# Baseline CRP
logit += 0.02 * patient.baseline_crp_mg_l
# Disease duration
logit += 0.03 * patient.disease_duration_years
# Smoking
if patient.smoking:
logit += 0.4
# BMI >30
if patient.bmi > 30:
logit += 0.05 * (patient.bmi - 30)
# Sigmoid
prob = 1.0 / (1.0 + math.exp(-logit))
score = int(prob * 100)
# Risk tier
if score <= 25:
tier = "Low"
recommendation = "Standard TDM at 6 months. Current regimen appropriate."
tdm_weeks = 26
elif score <= 50:
tier = "Moderate"
recommendation = (
"Schedule TDM at 3 months. "
"Ensure methotrexate ≥10 mg/week if tolerated. "
"Monitor trough drug levels."
)
tdm_weeks = 12
elif score <= 75:
tier = "High"
recommendation = (
"Proactive TDM at 6 weeks. Maximize methotrexate to 15–25 mg/week (subcutaneous preferred). "
"Obtain trough levels before any dose escalation. "
"Consider switching to pegylated construct (certolizumab) if ADA confirmed."
)
tdm_weeks = 6
else:
tier = "Very High"
recommendation = (
"Consider alternative mechanism of action (IL-6R: tocilizumab/sarilumab, JAKi: tofacitinib/upadacitinib, "
"CD20: rituximab). If TNFi required, use certolizumab (Fab', lower immunogenicity) "
"with proactive TDM at 4 weeks. HLA-DQA1*05 testing if not done."
)
tdm_weeks = 4
return {
"biologic": patient.biologic,
"ada_probability": round(prob, 4),
"risk_score": score,
"risk_tier": tier,
"recommended_tdm_weeks": tdm_weeks,
"recommendation": recommendation,
"factors": {
"monoclonal_ab": patient.biologic in MONOCLONAL_ABS,
"mtx_protection": patient.concomitant_mtx and patient.mtx_dose_mg_wk >= 10,
"hla_dqa1_05": patient.hla_dqa1_05,
"prior_failures": patient.prior_biologic_failures,
"crp": patient.baseline_crp_mg_l,
"disease_years": patient.disease_duration_years,
"smoking": patient.smoking,
"bmi": patient.bmi,
},
}
def monte_carlo_sensitivity(patient: PatientProfile, n_sim: int = 5000) -> dict:
"""Monte Carlo sensitivity analysis varying uncertain parameters."""
rng = np.random.default_rng(42)
scores = []
for _ in range(n_sim):
p = PatientProfile(
biologic=patient.biologic,
is_monoclonal_ab=patient.is_monoclonal_ab,
concomitant_mtx=patient.concomitant_mtx,
mtx_dose_mg_wk=patient.mtx_dose_mg_wk,
hla_dqa1_05=patient.hla_dqa1_05,
prior_biologic_failures=patient.prior_biologic_failures,
baseline_crp_mg_l=max(0, rng.normal(patient.baseline_crp_mg_l, patient.baseline_crp_mg_l * 0.2)),
disease_duration_years=patient.disease_duration_years,
smoking=patient.smoking,
bmi=max(15, rng.normal(patient.bmi, 2)),
)
result = compute_ada_risk(p)
scores.append(result["risk_score"])
scores = np.array(scores)
return {
"mean_score": float(np.mean(scores)),
"std_score": float(np.std(scores)),
"ci_95": [float(np.percentile(scores, 2.5)), float(np.percentile(scores, 97.5))],
"p_high_risk": float(np.mean(scores > 50)),
"n_simulations": n_sim,
}
def demo():
"""Run demo with 3 clinical scenarios."""
print("=" * 70)
print("ADA-Predictor: Anti-Drug Antibody Risk Stratification")
print("RheumaAI · Frutero Club · DeSci")
print("=" * 70)
scenarios = [
("RA patient starting adalimumab, no MTX, HLA+ carrier", PatientProfile(
biologic="adalimumab",
concomitant_mtx=False,
hla_dqa1_05=True,
prior_biologic_failures=0,
baseline_crp_mg_l=18.0,
disease_duration_years=3.0,
smoking=False,
bmi=27.0,
)),
("RA patient on infliximab + MTX 15mg/wk, HLA unknown", PatientProfile(
biologic="infliximab",
concomitant_mtx=True,
mtx_dose_mg_wk=15.0,
hla_dqa1_05=None,
prior_biologic_failures=1,
baseline_crp_mg_l=8.0,
disease_duration_years=7.0,
smoking=True,
bmi=32.0,
)),
("AS patient on etanercept + MTX 10mg/wk, HLA negative", PatientProfile(
biologic="etanercept",
concomitant_mtx=True,
mtx_dose_mg_wk=10.0,
hla_dqa1_05=False,
prior_biologic_failures=0,
baseline_crp_mg_l=4.0,
disease_duration_years=1.5,
smoking=False,
bmi=24.0,
)),
]
for label, patient in scenarios:
print(f"\n{'─' * 60}")
print(f"Scenario: {label}")
print(f"{'─' * 60}")
result = compute_ada_risk(patient)
print(f" Biologic: {result['biologic']}")
print(f" ADA Prob: {result['ada_probability']:.1%}")
print(f" Risk Score: {result['risk_score']}/100")
print(f" Risk Tier: {result['risk_tier']}")
print(f" TDM at: {result['recommended_tdm_weeks']} weeks")
print(f" Rec: {result['recommendation']}")
mc = monte_carlo_sensitivity(patient)
print(f" MC Mean±SD: {mc['mean_score']:.1f} ± {mc['std_score']:.1f}")
print(f" MC 95% CI: [{mc['ci_95'][0]:.0f}, {mc['ci_95'][1]:.0f}]")
print(f" P(High Risk): {mc['p_high_risk']:.1%}")
print(f"\n{'=' * 70}")
print("✅ All scenarios computed successfully.")
if __name__ == "__main__":
demo()
💡 Examples
python3 ada_predictor.py