diff --git a/IMPLEMENTATION_PLAN.md b/IMPLEMENTATION_PLAN.md
index 1af4ffb..77ccdb1 100644
--- a/IMPLEMENTATION_PLAN.md
+++ b/IMPLEMENTATION_PLAN.md
@@ -510,15 +510,15 @@ Additionally: KPI row removed, fraction KPIs moved to header, global filter sub-
 - **Checkpoint**: Patient Pathways shows only Icicle + Sankey tabs, both still work with all existing filters
 
 ### 10.6 Trust Comparison query functions
-- [ ] Add new/modified query functions to `src/data_processing/pathway_queries.py` for per-trust-within-directorate perspective:
+- [x] Add new/modified query functions to `src/data_processing/pathway_queries.py` for per-trust-within-directorate perspective:
   - `get_trust_market_share(db_path, filter_id, chart_type, directory)` — drugs by trust within a single directorate (stacked bars per trust instead of per directorate)
   - `get_trust_cost_waterfall(db_path, filter_id, chart_type, directory)` — one bar per trust showing cost_pp within that directorate
   - `get_trust_dosing(db_path, filter_id, chart_type, directory)` — drug dosing intervals broken down by trust within a directorate
   - `get_trust_heatmap(db_path, filter_id, chart_type, directory)` — trust × drug matrix for one directorate (rows=trusts, cols=drugs)
   - `get_trust_durations(db_path, filter_id, chart_type, directory)` — drug durations by trust within a directorate
-  - `get_directorate_pathway_costs(db_path, filter_id, chart_type, directory)` — pathway costs filtered to one directorate (same as existing `get_pathway_costs` with directory param, but verify it works correctly)
-- [ ] Add thin wrappers in `dash_app/data/queries.py`
-- [ ] Verify all queries return correct data for both "directory" and "indication" chart types
+  - `get_directorate_pathway_costs(db_path, filter_id, chart_type, directory)` — verified existing `get_pathway_costs(directory=X)` works correctly (no new function needed)
+- [x] Add thin wrappers in `dash_app/data/queries.py`
+- [x] Verify all queries return correct data for both "directory" and "indication" chart types
 - **Checkpoint**: All 6 query functions return correct per-trust data for sample directorates
 
 ### 10.7 Trust Comparison landing page + directorate selector
diff --git a/dash_app/data/queries.py b/dash_app/data/queries.py
index bfc4d32..63a7566 100644
--- a/dash_app/data/queries.py
+++ b/dash_app/data/queries.py
@@ -18,6 +18,11 @@ from data_processing.pathway_queries import (
     get_dosing_intervals as _get_dosing_intervals,
     get_drug_directory_matrix as _get_drug_directory_matrix,
     get_treatment_durations as _get_treatment_durations,
+    get_trust_market_share as _get_trust_market_share,
+    get_trust_cost_waterfall as _get_trust_cost_waterfall,
+    get_trust_dosing as _get_trust_dosing,
+    get_trust_heatmap as _get_trust_heatmap,
+    get_trust_durations as _get_trust_durations,
 )
 
 DB_PATH = Path(__file__).resolve().parents[2] / "data" / "pathways.db"
@@ -115,3 +120,51 @@ def get_treatment_durations(
 ) -> list[dict]:
     """Treatment duration data (avg_days) by drug."""
     return _get_treatment_durations(DB_PATH, date_filter_id, chart_type, directory, trust)
+
+
+# --- Trust Comparison query wrappers (Phase 10) ---
+
+
+def get_trust_market_share(
+    date_filter_id: str = "all_6mo",
+    chart_type: str = "directory",
+    directory: str = "",
+) -> list[dict]:
+    """Drug market share by trust within a single directorate."""
+    return _get_trust_market_share(DB_PATH, date_filter_id, chart_type, directory)
+
+
+def get_trust_cost_waterfall(
+    date_filter_id: str = "all_6mo",
+    chart_type: str = "directory",
+    directory: str = "",
+) -> list[dict]:
+    """Cost per patient by trust within a single directorate."""
+    return _get_trust_cost_waterfall(DB_PATH, date_filter_id, chart_type, directory)
+
+
+def get_trust_dosing(
+    date_filter_id: str = "all_6mo",
+    chart_type: str = "directory",
+    directory: str = "",
+) -> list[dict]:
+    """Drug dosing intervals by trust within a single directorate."""
+    return _get_trust_dosing(DB_PATH, date_filter_id, chart_type, directory)
+
+
+def get_trust_heatmap(
+    date_filter_id: str = "all_6mo",
+    chart_type: str = "directory",
+    directory: str = "",
+) -> dict:
+    """Trust x drug matrix for a single directorate."""
+    return _get_trust_heatmap(DB_PATH, date_filter_id, chart_type, directory)
+
+
+def get_trust_durations(
+    date_filter_id: str = "all_6mo",
+    chart_type: str = "directory",
+    directory: str = "",
+) -> list[dict]:
+    """Drug durations by trust within a single directorate."""
+    return _get_trust_durations(DB_PATH, date_filter_id, chart_type, directory)
diff --git a/src/data_processing/pathway_queries.py b/src/data_processing/pathway_queries.py
index 424ac16..de36662 100644
--- a/src/data_processing/pathway_queries.py
+++ b/src/data_processing/pathway_queries.py
@@ -812,3 +812,255 @@ def get_treatment_durations(
         return []
     finally:
         conn.close()
+
+
+# ---------------------------------------------------------------------------
+# Trust Comparison query functions (Phase 10)
+#
+# These functions provide per-trust breakdowns within a single directorate.
+# Unlike Phase 9 functions which aggregate across trusts, these preserve
+# per-trust detail for comparing drugs across trusts within a directorate.
+# ---------------------------------------------------------------------------
+
+
+def get_trust_market_share(
+    db_path: Path,
+    date_filter_id: str,
+    chart_type: str,
+    directory: str,
+) -> list[dict]:
+    """Drug market share broken down by trust within a single directorate.
+
+    Returns list of dicts: [{trust_name, drug, patients, proportion, cost, cost_pp_pa}]
+    Sorted by trust total patients desc, then drug patients desc within each trust.
+    """
+    conn = sqlite3.connect(str(db_path))
+    conn.row_factory = sqlite3.Row
+    try:
+        query = """
+            SELECT labels AS drug, trust_name, value AS patients, cost, cost_pp_pa
+            FROM pathway_nodes
+            WHERE date_filter_id = ? AND chart_type = ? AND level = 3
+                AND directory = ?
+            ORDER BY trust_name, value DESC
+        """
+        rows = conn.execute(query, (date_filter_id, chart_type, directory)).fetchall()
+
+        # Compute proportions within each trust
+        trust_totals = {}
+        for r in rows:
+            trust = r["trust_name"] or ""
+            trust_totals[trust] = trust_totals.get(trust, 0) + (r["patients"] or 0)
+
+        result = []
+        for r in rows:
+            trust = r["trust_name"] or ""
+            patients = r["patients"] or 0
+            cost = float(r["cost"]) if r["cost"] else 0.0
+            total = trust_totals.get(trust, 1)
+            result.append({
+                "trust_name": trust,
+                "drug": r["drug"],
+                "patients": patients,
+                "proportion": round(patients / total, 4) if total else 0,
+                "cost": round(cost, 2),
+                "cost_pp_pa": _safe_float(r["cost_pp_pa"]),
+            })
+
+        result.sort(key=lambda x: (-trust_totals.get(x["trust_name"], 0), -x["patients"]))
+        return result
+    except sqlite3.Error:
+        return []
+    finally:
+        conn.close()
+
+
+def get_trust_cost_waterfall(
+    db_path: Path,
+    date_filter_id: str,
+    chart_type: str,
+    directory: str,
+) -> list[dict]:
+    """Cost per patient by trust within a single directorate.
+
+    Returns list of dicts: [{trust_name, patients, total_cost, cost_pp}]
+    Sorted by cost_pp desc.
+    """
+    conn = sqlite3.connect(str(db_path))
+    conn.row_factory = sqlite3.Row
+    try:
+        query = """
+            SELECT trust_name, SUM(value) AS patients, SUM(cost) AS total_cost
+            FROM pathway_nodes
+            WHERE date_filter_id = ? AND chart_type = ? AND level = 3
+                AND directory = ?
+            GROUP BY trust_name
+            HAVING patients > 0
+            ORDER BY total_cost DESC
+        """
+        rows = conn.execute(query, (date_filter_id, chart_type, directory)).fetchall()
+
+        result = []
+        for r in rows:
+            patients = r["patients"] or 0
+            total_cost = float(r["total_cost"]) if r["total_cost"] else 0.0
+            result.append({
+                "trust_name": r["trust_name"] or "",
+                "patients": patients,
+                "total_cost": round(total_cost, 2),
+                "cost_pp": round(total_cost / patients, 2) if patients else 0,
+            })
+
+        result.sort(key=lambda x: -x["cost_pp"])
+        return result
+    except sqlite3.Error:
+        return []
+    finally:
+        conn.close()
+
+
+def get_trust_dosing(
+    db_path: Path,
+    date_filter_id: str,
+    chart_type: str,
+    directory: str,
+) -> list[dict]:
+    """Drug dosing intervals by trust within a single directorate.
+
+    Returns list of dicts:
+        [{drug, trust_name, weekly_interval, dose_count, total_weeks, patients}]
+    """
+    from data_processing.parsing import parse_average_spacing
+
+    conn = sqlite3.connect(str(db_path))
+    conn.row_factory = sqlite3.Row
+    try:
+        query = """
+            SELECT labels AS drug, trust_name, value AS patients, average_spacing
+            FROM pathway_nodes
+            WHERE date_filter_id = ? AND chart_type = ? AND level = 3
+                AND directory = ?
+                AND average_spacing IS NOT NULL AND average_spacing != ''
+            ORDER BY labels, trust_name
+        """
+        rows = conn.execute(query, (date_filter_id, chart_type, directory)).fetchall()
+
+        result = []
+        for r in rows:
+            parsed = parse_average_spacing(r["average_spacing"])
+            for entry in parsed:
+                result.append({
+                    "drug": entry["drug_name"],
+                    "trust_name": r["trust_name"] or "",
+                    "weekly_interval": entry["weekly_interval"],
+                    "dose_count": entry["dose_count"],
+                    "total_weeks": entry["total_weeks"],
+                    "patients": r["patients"] or 0,
+                })
+
+        return result
+    except sqlite3.Error:
+        return []
+    finally:
+        conn.close()
+
+
+def get_trust_heatmap(
+    db_path: Path,
+    date_filter_id: str,
+    chart_type: str,
+    directory: str,
+) -> dict:
+    """Trust x drug matrix for a single directorate.
+
+    Returns dict with:
+        trusts: sorted list of trust names (rows)
+        drugs: sorted list of drug names (columns)
+        matrix: {trust_name: {drug: {patients, cost, cost_pp_pa}}}
+    """
+    conn = sqlite3.connect(str(db_path))
+    conn.row_factory = sqlite3.Row
+    try:
+        query = """
+            SELECT labels AS drug, trust_name, value AS patients, cost, cost_pp_pa
+            FROM pathway_nodes
+            WHERE date_filter_id = ? AND chart_type = ? AND level = 3
+                AND directory = ?
+            ORDER BY trust_name, labels
+        """
+        rows = conn.execute(query, (date_filter_id, chart_type, directory)).fetchall()
+
+        matrix = {}
+        trust_totals = {}
+        drug_totals = {}
+
+        for r in rows:
+            trust = r["trust_name"] or ""
+            drug = r["drug"] or ""
+            patients = r["patients"] or 0
+            cost = float(r["cost"]) if r["cost"] else 0.0
+
+            if trust not in matrix:
+                matrix[trust] = {}
+            matrix[trust][drug] = {
+                "patients": patients,
+                "cost": round(cost, 2),
+                "cost_pp_pa": _safe_float(r["cost_pp_pa"]),
+            }
+
+            trust_totals[trust] = trust_totals.get(trust, 0) + patients
+            drug_totals[drug] = drug_totals.get(drug, 0) + patients
+
+        trusts = sorted(trust_totals, key=lambda x: -trust_totals[x])
+        drugs = sorted(drug_totals, key=lambda x: -drug_totals[x])
+
+        return {"trusts": trusts, "drugs": drugs, "matrix": matrix}
+    except sqlite3.Error:
+        return {"trusts": [], "drugs": [], "matrix": {}}
+    finally:
+        conn.close()
+
+
+def get_trust_durations(
+    db_path: Path,
+    date_filter_id: str,
+    chart_type: str,
+    directory: str,
+) -> list[dict]:
+    """Drug treatment durations by trust within a single directorate.
+
+    Returns list of dicts: [{drug, trust_name, avg_days, patients}]
+    Sorted by drug name, then trust. No aggregation — each row is one
+    trust+drug combination.
+    """
+    conn = sqlite3.connect(str(db_path))
+    conn.row_factory = sqlite3.Row
+    try:
+        query = """
+            SELECT labels AS drug, trust_name, value AS patients, avg_days
+            FROM pathway_nodes
+            WHERE date_filter_id = ? AND chart_type = ? AND level = 3
+                AND directory = ?
+                AND avg_days IS NOT NULL
+            ORDER BY labels, trust_name
+        """
+        rows = conn.execute(query, (date_filter_id, chart_type, directory)).fetchall()
+
+        result = []
+        for r in rows:
+            patients = r["patients"] or 0
+            days = _safe_float(r["avg_days"])
+            if patients == 0 or days == 0:
+                continue
+            result.append({
+                "drug": r["drug"],
+                "trust_name": r["trust_name"] or "",
+                "avg_days": round(days, 1),
+                "patients": patients,
+            })
+
+        return result
+    except sqlite3.Error:
+        return []
+    finally:
+        conn.close()