v0.5.62 — Pre-Registered Pass/Fail Criteria (Loop 41)

configs/wave1_pass_fail.yaml

@@ -0,0 +1,69 @@
+# Wave 1 Wet-Lab Pass/Fail Criteria
+#
+# Pre-registered before any wet-lab data is seen.
+# Machine-readable gates checked by check_wave1_pass_fail.py.
+# Locked: do not modify without a signed deviation record.
+
+pipeline_version: "0.5.x"
+locked_at: "2026-07-06"
+locked_by: "OpenAMP agent (pre-registration template)"
+
+# Primary endpoint: MIC ≤ 32 ug/mL against E. coli ATCC 25922
+primary_endpoint:
+  organism: "Escherichia coli ATCC 25922"
+  assay_type: MIC
+  max_mic_ugml: 32
+  min_passing_candidates: 3
+
+# Secondary endpoint: MIC ≤ 32 ug/mL and TI > 10 (selective hit)
+secondary_endpoint:
+  max_mic_ugml: 32
+  min_therapeutic_index: 10
+  min_passing_candidates: 1
+
+# Confirmed hit definition
+confirmed_hit:
+  mic_ugml_max: 32
+  ti_min: 10
+  positive_control_required: true
+  min_confirmed_hits: 1
+
+# Hemolysis safety gate
+hemolysis:
+  assay_type: hemolysis_RBC
+  # Therapeutic index = HC50 / MIC. TI > 10 = selective.
+  # TI < 2 = toxic (exclude from further consideration).
+  ti_toxic_threshold: 2
+  ti_selective_threshold: 10
+  # No candidate with TI < 2 is acceptable in a passing batch
+  max_toxic_candidates: 0
+
+# Serum stability (optional, informational)
+serum_stability:
+  assay_type: serum_stability
+  pass_criterion: "> 50% intact at 60 min"
+  # Informational only — does not gate the batch
+
+# Positive control requirements
+positive_controls:
+  - candidate_id: "SEED-001_VAR_064"
+    expected_mic_min_ugml: 4
+    expected_mic_max_ugml: 32
+    description: "Magainin-1 derivative — validates assay is working"
+
+# Batch-level acceptance
+batch_level:
+  # Batch is valid only if all these pass
+  positive_control_passed: true
+  negative_control_passed: true
+  min_total_candidates_tested: 20
+  # No data points excluded without documented justification
+  require_all_results_reported: true
+
+# Negative result reporting
+negative_result_archive:
+  # All tested candidates must have entries, even if inactive
+  # Archive to outputs/negative_result_archive.csv via calibration-intake
+  require_inactive_reported: true
+  require_toxic_reported: true
+  require_control_failures_reported: true

docs/50_LOOP_PLAN.md

@@ -126,7 +126,7 @@ Prepare for real wet-lab partners. Make the pack reviewable, the evidence tracea
 | Loop | Bottleneck | Deliverable | Verification |
 |------|-----------|-------------|-------------|
 | 40 ✅ | No assay-partner onboarding pack. A new CRO gets no README for how to run the panel | `docs/LAB_PARTNER_ONBOARDING.md`: panel manifest, synthesis instructions, assay protocol references, data return schema, positive/negative control sequences, safety notes, timeline, controls. v0.5.61 | A new CRO can read this and know exactly what to do |
-| 41 | No pre-registered pass/fail criteria for the Wave 1 wet-lab batch. ASSAY_PREREGISTRATION exists but criteria are not machine-checkable | `configs/wave1_pass_fail.yaml`: machine-readable pass/fail gates for Wave 1 (MIC ≤ 32 for ≥ N candidates, hemolysis ≤ 10% for ≥ M candidates, at least 1 control passes) | CI validates criteria against lab result schema |
+| 41 ✅ | No pre-registered pass/fail criteria for the Wave 1 wet-lab batch. ASSAY_PREREGISTRATION exists but criteria are not machine-checkable | `configs/wave1_pass_fail.yaml`: machine-readable pass/fail gates (MIC ≤ 32 for ≥ 3 candidates, TI > 10 for ≥ 1, positive control within range, 0 toxic, ≥ 20 tested). `scripts/check_wave1_pass_fail.py`: validation CLI, exit 0/3/2. v0.5.62 | 11 tests, 1977 total. CLI validates synthetic results against YAML criteria |
 | 42 | No expert-review GitHub template. Review process is ad-hoc | `.github/ISSUE_TEMPLATE/expert_review.yml`: structured form for expert reviewers covering novelty, safety, mechanism plausibility, experiment design | Template generates a review issue with all required fields |
 | 43 | No decision-log format for human review. AGENTS.md requires human review for 6 decision types but no machine-readable log format exists | `schemas/decision_log.schema.json`: JSON Schema for human review decisions. Fields: date, reviewer, decision_type, evidence_refs, reasoning_notes, dissent_flag | Schema validates against real decision examples |
 | 44 | No candidate batch pack that a lab partner can download as a single artifact | `scripts/build_lab_batch_pack.py`: generates a zip with candidate CSV, evidence certificates, synthesis order, assay protocol refs, controls manifest, and data return template | `make lab-batch-pack` produces a single zip with all required files |
@@ -242,7 +242,7 @@ Phase 4: In progress — Loop 40 ✅ (Loops 40–49)
 | 28 ✅ | Policy version bump workflow for when real data arrives | `scripts/bump_recalibration_policy.py`: standalone script with `--dry-run`, decision-log guard, auto-increment + write. CI guard in `ci.yml` validates policy version changes against base branch. v0.5.49. 9 tests. | CI rejects policy PRs without valid decision log; `make bump-policy-version` and `make bump-policy-version-dry-run` available |
 | 29 ✅ | No public negative-result archive format. If Wave 1 yields all negatives, where do they go? | `docs/NEGATIVE_RESULT_ARCHIVE.md`: full template with entry schema, procedures, automation notes, and limitations. Covers pre-selection rejects, selected-untested, lab inactives, lab toxic, control failures. v0.5.50. | Template complete enough for a lab partner to fill; schema defines 18 fields with required/conditional markers |
 
-**Next loop:** Loop 41 — pre-registered pass/fail criteria (configs/wave1_pass_fail.yaml).
+**Next loop:** Loop 42 — expert-review GitHub template.
 
 **Phase 2 exit criteria (all 5 met ✅):**
 - ✅ `make calibration-loop` runs from clean checkout, produces batch-2 manifest

scripts/check_wave1_pass_fail.py

@@ -0,0 +1,207 @@
+"""Check Wave 1 lab results against pre-registered pass/fail criteria.
+
+Reads lab result JSON files from a directory, compares against the
+pre-registered criteria in configs/wave1_pass_fail.yaml, and reports
+which criteria pass or fail.
+
+Exit codes:
+  0: All criteria pass
+  3: One or more criteria fail
+  2: Missing files or parse error
+"""
+
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+
+def _load_lab_results(results_dir: str) -> list[dict[str, Any]]:
+    results = []
+    p = Path(results_dir)
+    if not p.exists():
+        raise FileNotFoundError(f"Results directory not found: {results_dir}")
+    for f in sorted(p.iterdir()):
+        if f.suffix.lower() in (".json",):
+            data = json.loads(f.read_text(encoding="utf-8"))
+            results.append(data)
+    return results
+
+
+def _check_criteria(
+    results: list[dict[str, Any]],
+    criteria: dict[str, Any],
+) -> dict[str, Any]:
+    checks: dict[str, Any] = {}
+    all_pass = True
+
+    # Primary endpoint: MIC <= 32 for >= N candidates
+    pri = criteria.get("primary_endpoint", {})
+    max_mic = pri.get("max_mic_ugml", 32)
+    min_pass = pri.get("min_passing_candidates", 3)
+    organism = pri.get("organism", "")
+
+    passing_mic = sum(
+        1 for r in results
+        if r.get("assay_type") == "MIC"
+        and r.get("result_value", 999) <= max_mic
+        and (not organism or organism in r.get("organism_or_cell_line", ""))
+    )
+    mic_pass = passing_mic >= min_pass
+    checks["primary_endpoint_mic"] = {
+        "pass": mic_pass,
+        "detail": f"{passing_mic}/{min_pass} candidates with MIC <= {max_mic} ug/mL",
+    }
+    if not mic_pass:
+        all_pass = False
+
+    # Positive control check
+    pos_controls = criteria.get("positive_controls", [])
+    pos_pass = True
+    pos_details = []
+    for pc in pos_controls:
+        cid = pc.get("candidate_id", "")
+        expected_min = pc.get("expected_mic_min_ugml", 0)
+        expected_max = pc.get("expected_mic_max_ugml", 999)
+        matches = [
+            r for r in results
+            if r.get("candidate_id") == cid and r.get("assay_type") == "MIC"
+        ]
+        if not matches:
+            pos_pass = False
+            pos_details.append(f"{cid}: no results found")
+        else:
+            for m in matches:
+                val = m.get("result_value", -1)
+                if val < expected_min or val > expected_max:
+                    pos_pass = False
+                    pos_details.append(f"{cid}: MIC={val} outside expected [{expected_min}, {expected_max}]")
+                else:
+                    pos_details.append(f"{cid}: MIC={val} within expected range")
+    checks["positive_controls"] = {
+        "pass": pos_pass,
+        "detail": "; ".join(pos_details) if pos_details else "No positive controls defined",
+    }
+    if not pos_pass:
+        all_pass = False
+
+    # Hemolysis safety: no toxic candidates
+    hemo = criteria.get("hemolysis", {})
+    toxic_threshold = hemo.get("ti_toxic_threshold", 2)
+    max_toxic = hemo.get("max_toxic_candidates", 0)
+    toxic_count = 0
+    hemo_details = []
+
+    for r in results:
+        if r.get("assay_type") == "hemolysis_RBC":
+            hc50 = r.get("result_value", 0)
+            cid = r.get("candidate_id", "")
+            mic_results = [
+                x for x in results
+                if x.get("candidate_id") == cid and x.get("assay_type") == "MIC"
+            ]
+            if mic_results:
+                mic_val = min(x.get("result_value", 999) for x in mic_results)
+                if mic_val > 0:
+                    ti = hc50 / mic_val
+                    if ti < toxic_threshold:
+                        toxic_count += 1
+                        hemo_details.append(f"{cid}: TI={ti:.2f} (toxic)")
+                    elif ti < hemo.get("ti_selective_threshold", 10):
+                        hemo_details.append(f"{cid}: TI={ti:.2f} (borderline)")
+                    else:
+                        hemo_details.append(f"{cid}: TI={ti:.2f} (selective)")
+
+    hemo_pass = toxic_count <= max_toxic
+    checks["hemolysis_safety"] = {
+        "pass": hemo_pass,
+        "detail": f"{toxic_count} toxic (TI < {toxic_threshold}), max allowed {max_toxic}. " + "; ".join(hemo_details[:5]),
+    }
+    if not hemo_pass:
+        all_pass = False
+
+    # Batch-level: positive/negative controls passed
+    batch = criteria.get("batch_level", {})
+    total_tested = len(set(r.get("candidate_id", "") for r in results))
+    batch_pass = total_tested >= batch.get("min_total_candidates_tested", 1)
+    checks["batch_min_tested"] = {
+        "pass": batch_pass,
+        "detail": f"{total_tested} candidates tested (minimum {batch.get('min_total_candidates_tested', 1)})",
+    }
+    if not batch_pass:
+        all_pass = False
+
+    # Secondary endpoint
+    sec = criteria.get("secondary_endpoint", {})
+    sec_max_mic = sec.get("max_mic_ugml", 32)
+    sec_min_ti = sec.get("min_therapeutic_index", 10)
+    sec_min = sec.get("min_passing_candidates", 1)
+    sec_count = 0
+    for r in results:
+        if r.get("assay_type") == "MIC" and r.get("result_value", 999) <= sec_max_mic:
+            cid = r.get("candidate_id", "")
+            hemo_results = [
+                x for x in results
+                if x.get("candidate_id") == cid and x.get("assay_type") == "hemolysis_RBC"
+            ]
+            if hemo_results:
+                hc50 = hemo_results[0].get("result_value", 0)
+                mic_val = r.get("result_value", 999)
+                if mic_val > 0 and (hc50 / mic_val) > sec_min_ti:
+                    sec_count += 1
+    sec_pass = sec_count >= sec_min
+    checks["secondary_endpoint"] = {
+        "pass": sec_pass,
+        "detail": f"{sec_count}/{sec_min} candidates with MIC <= {sec_max_mic} and TI > {sec_min_ti}",
+    }
+    if not sec_pass:
+        all_pass = False
+
+    return {"all_pass": all_pass, "checks": checks}
+
+
+def main() -> int:
+    import argparse
+    parser = argparse.ArgumentParser(description="Check Wave 1 pass/fail criteria")
+    parser.add_argument("--results-dir", required=True, help="Directory with lab result JSONs")
+    parser.add_argument("--criteria", default="configs/wave1_pass_fail.yaml", help="Pass/fail criteria YAML")
+    parser.add_argument("--out", default=None, help="JSON output path")
+    args = parser.parse_args()
+
+    try:
+        results = _load_lab_results(args.results_dir)
+    except FileNotFoundError as e:
+        print(f"Error: {e}", file=sys.stderr)
+        return 2
+
+    criteria_p = Path(args.criteria)
+    if not criteria_p.exists():
+        print(f"Error: Criteria file not found: {args.criteria}", file=sys.stderr)
+        return 2
+
+    criteria = yaml.safe_load(criteria_p.read_text(encoding="utf-8"))
+    if not results:
+        print("Error: No lab results found", file=sys.stderr)
+        return 2
+
+    verdict = _check_criteria(results, criteria)
+    output = json.dumps(verdict, indent=2)
+    print(output)
+
+    if args.out:
+        Path(args.out).write_text(output + "\n", encoding="utf-8")
+
+    if verdict["all_pass"]:
+        print("\nAll criteria PASS")
+        return 0
+    else:
+        print("\nOne or more criteria FAIL")
+        return 3
+
+
+if __name__ == "__main__":
+    sys.exit(main())