m06_treatment_outcome_model.py

"""
Module 06: Treatment Outcome Model and Study Design
====================================================

Models expected clinical outcomes and proposes a study design
to test the bromantane-xenon combination hypothesis.

Includes:
- PANSS score improvement predictions
- Symptom domain-specific effects
- Dosing regimen recommendations
- Proposed clinical trial design
- Risk-benefit analysis
"""

import numpy as np
import json
from dataclasses import dataclass
from typing import Dict, List


@dataclass
class TrialDesign:
    """Proposed clinical trial design parameters."""
    name: str
    phase: str
    n_patients: int
    duration_weeks: int
    arms: List[str]
    primary_outcome: str
    secondary_outcomes: List[str]


class TreatmentOutcomeModel:
    """
    Models expected treatment outcomes and proposes study designs.
    """

    def __init__(self):
        self.results = {}

    def predict_panss_improvement(self, m03_mc: Dict = None) -> Dict:
        """
        Predict PANSS (Positive and Negative Syndrome Scale) improvement.
        
        PANSS has 3 subscales:
        - Positive (P): delusions, hallucinations (1-7 per item, 7 items)
        - Negative (N): avolition, alogia, flat affect (1-7 per item, 7 items)
        - General (G): anxiety, guilt, depression, disorganization (1-7 per item, 16 items)
        
        Typical chronic schizophrenia: P=25, N=30, G=50 (total ~105)
        Treatment response: antipsychotics reduce P by ~30%, N by ~10-15%
        """
        if m03_mc is None:
            from m03_synergistic_interaction import run_module
            m03 = run_module()
            m03_mc = m03["monte_carlo"]

        # Baseline PANSS (chronic, treatment-resistant schizophrenia)
        baseline = {
            "positive": 25.0,
            "negative": 30.0,
            "general": 50.0,
            "total": 105.0
        }

        # Improvement predictions (from Monte Carlo)
        positive_improvement = m03_mc["positive_symptoms"]["combined_effect"]
        negative_improvement = m03_mc["negative_symptoms"]["combined_effect"]
        general_improvement = m03_mc["cognitive_symptoms"]["combined_effect"] * 0.7  # Partial overlap

        # PANSS score changes (max improvement per subscale)
        panss_positive_max = 7 * 7  # 7 items * (7-1) max change per item
        panss_negative_max = 7 * 6
        panss_general_max = 16 * 6

        p_positive_score = round(baseline["positive"] * (1 - positive_improvement), 1)
        p_negative_score = round(baseline["negative"] * (1 - negative_improvement), 1)
        p_general_score = round(baseline["general"] * (1 - general_improvement * 0.7), 1)
        p_total_score = round(p_positive_score + p_negative_score + p_general_score, 1)
        p_total_change = round(
            -baseline["positive"] * positive_improvement -
            baseline["negative"] * negative_improvement -
            baseline["general"] * general_improvement * 0.7, 1
        )

        predicted = {
            "positive": {
                "baseline": baseline["positive"],
                "improvement_pct": float(positive_improvement),
                "predicted_score": p_positive_score,
                "change": round(-baseline["positive"] * positive_improvement, 1)
            },
            "negative": {
                "baseline": baseline["negative"],
                "improvement_pct": float(negative_improvement),
                "predicted_score": p_negative_score,
                "change": round(-baseline["negative"] * negative_improvement, 1)
            },
            "general": {
                "baseline": baseline["general"],
                "improvement_pct": float(general_improvement),
                "predicted_score": p_general_score,
                "change": round(-baseline["general"] * general_improvement * 0.7, 1)
            },
            "total": {
                "baseline": baseline["total"],
                "predicted_score": p_total_score,
                "change": p_total_change
            }
        }

        self.results["panss_prediction"] = predicted
        return predicted

    def propose_trial_design(self) -> Dict:
        """
        Propose a Phase II clinical trial to test the hypothesis.
        """
        design = {
            "trial_1_phase_ii_proof_of_concept": {
                "name": "Bromantane-Xenon in Schizophrenia Negative Symptoms",
                "phase": "II",
                "n_patients": 120,
                "duration_weeks": 12,
                "arms": [
                    "Bromantane 100mg/day + Placebo inhalation",
                    "Xenon 50% inhalation (3x/week) + Placebo pill",
                    "Bromantane 100mg/day + Xenon 50% inhalation (3x/week)",
                    "Active control: Risperidone 4mg/day"
                ],
                "primary_outcome": "Change in PANSS negative subscale at 12 weeks",
                "secondary_outcomes": [
                    "PANSS positive subscale change",
                    "PANSS total score change",
                    "COGNS (cognitive battery) change",
                    "Subjective negative symptom scale",
                    "Quality of life questionnaire"
                ],
                "inclusion": "Chronic schizophrenia, PANSS-N >= 25, stable on antipsychotics",
                "exclusion": "History of substance abuse, NMDA antagonist use in past 3 months",
                "hypothesis": "Combination arm shows greater PANSS-N improvement than either monotherapy"
            },
            "trial_2_phase_ii_acute_exacerbation": {
                "name": "Xenon Rescue for Acute Schizophrenia Exacerbations",
                "phase": "II",
                "n_patients": 60,
                "duration_weeks": 4,
                "arms": [
                    "Xenon 60% inhalation (daily, 30 min)",
                    "Xenon 60% inhalation (every other day, 30 min)",
                    "Placebo inhalation"
                ],
                "primary_outcome": "Change in PANSS total score at 4 weeks",
                "secondary_outcomes": [
                    "CGI-S (clinical global impression)",
                    "Hospitalization rate",
                    "Adverse event profile"
                ],
                "inclusion": "Acute exacerbation, PANSS total >= 70",
                "exclusion": "History of xenon adverse reaction, respiratory disease"
            }
        }

        self.results["trial_design"] = design
        return design

    def risk_benefit_analysis(self, m03_mc: Dict = None) -> Dict:
        """
        Comprehensive risk-benefit analysis.
        """
        if m03_mc is None:
            from m03_synergistic_interaction import run_module
            m03 = run_module()
            m03_mc = m03["monte_carlo"]

        # Benefits
        benefit_negative = m03_mc["negative_symptoms"]["combined_effect"]
        benefit_positive = m03_mc["positive_symptoms"]["combined_effect"]
        benefit_cognitive = m03_mc["cognitive_symptoms"]["combined_effect"]
        therapeutic_index = m03_mc["therapeutic_index"]["mean"]

        # Risks
        # Bromantane: rare adverse events (~5%)
        # Xenon: anesthesia-related (~12%)
        # Combination: ~17% combined adverse rate
        # Theoretical risk: bromantane could worsen positive symptoms (low)
        # Theoretical risk: xenon could worsen NMDA hypofunction (low-moderate)
        adverse_rate = m03_mc["adverse_effects"]["combined_adverse_rate"]

        analysis = {
            "benefits": {
                "negative_symptom_improvement": float(benefit_negative),
                "positive_symptom_improvement": float(benefit_positive),
                "cognitive_improvement": float(benefit_cognitive),
                "therapeutic_index": float(therapeutic_index),
                "unique_advantages": [
                    "Targets both dopamine and glutamate hypotheses",
                    "Addresses negative symptoms (unmet need)",
                    "No tolerance development (bromantane)",
                    "Fast rescue capability (xenon)",
                    "Neuroprotective effects (xenon)",
                    "Genomic persistence (bromantane)"
                ]
            },
            "risks": {
                "combined_adverse_rate": float(adverse_rate),
                "theoretical_risks": [
                    "Bromantane may increase mesolimbic dopamine -> worsen positive symptoms",
                    "Xenon NMDA blockade may worsen interneuron disinhibition",
                    "Xenon requires anesthesia infrastructure",
                    "Bromantane not FDA-approved (Russia-only)",
                    "Xenon not FDA-approved for psychiatric use"
                ],
                "mitigation_strategies": [
                    "Start with low-dose bromantane (50mg)",
                    "Monitor PANSS positive subscale closely",
                    "Use xenon only in clinical setting with anesthesia support",
                    "Exclude patients with acute positive symptoms",
                    "Include cognitive safety assessments"
                ]
            },
            "overall_assessment": (
                "Favorable risk-benefit ratio for treatment-resistant negative symptoms."
                " Primary concern is theoretical worsening of positive symptoms; requires"
                " careful patient selection and monitoring. Neuroprotective effects of xenon"
                " and genomic persistence of bromantane provide unique advantages over"
                " existing treatments."
            )
        }

        self.results["risk_benefit"] = analysis
        return analysis


def run_module(m03_mc: Dict = None):
    """Run the full treatment outcome model."""
    print("=" * 70)
    print("MODULE 06: TREATMENT OUTCOME MODEL AND STUDY DESIGN")
    print("=" * 70)

    model = TreatmentOutcomeModel()

    # 1. PANSS prediction
    print("\n[1/3] Predicting PANSS improvement...")
    panss = model.predict_panss_improvement(m03_mc)
    print(f"  -> Positive: {panss['positive']['baseline']} -> {panss['positive']['predicted_score']} ({panss['positive']['change']:+.1f})")
    print(f"  -> Negative: {panss['negative']['baseline']} -> {panss['negative']['predicted_score']} ({panss['negative']['change']:+.1f})")
    print(f"  -> General: {panss['general']['baseline']} -> {panss['general']['predicted_score']} ({panss['general']['change']:+.1f})")
    print(f"  -> Total: {panss['total']['baseline']} -> {panss['total']['predicted_score']} ({panss['total']['change']:+.1f})")

    # 2. Trial design
    print("\n[2/3] Proposing trial designs...")
    trials = model.propose_trial_design()
    for name, trial in trials.items():
        print(f"  -> {trial['name']}")
        print(f"     Phase {trial['phase']}, N={trial['n_patients']}, {trial['duration_weeks']} weeks")
        print(f"     Primary: {trial['primary_outcome']}")

    # 3. Risk-benefit
    print("\n[3/3] Risk-benefit analysis...")
    rb = model.risk_benefit_analysis(m03_mc)
    print(f"  -> Adverse rate: {rb['risks']['combined_adverse_rate']:.1%}")
    print(f"  -> Therapeutic index: {rb['benefits']['therapeutic_index']:.1f}")
    print(f"  -> Assessment: {rb['overall_assessment'][:120]}...")

    return {
        "module": "m06_treatment_outcome_model",
        "panss_prediction": panss,
        "trial_design": trials,
        "risk_benefit": rb
    }


if __name__ == "__main__":
    run_module()