""" Generate a structured PDF analysis report for preclinical literature extraction. Creates a publication-quality PDF with Title Page, Introduction, Methods, Results (with embedded figures), and Conclusions sections using reportlab. Requires: reportlab (pip install reportlab) Falls back gracefully if reportlab is not installed. """ import os from datetime import datetime try: from reportlab.lib import colors from reportlab.lib.pagesizes import letter from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle from reportlab.lib.units import inch from reportlab.lib.enums import TA_CENTER from reportlab.platypus import ( SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle, Image, PageBreak, HRFlowable, ) HAS_REPORTLAB = True except ImportError: HAS_REPORTLAB = False # --------------------------------------------------------------------------- # Color constants (consistent with project standard) # --------------------------------------------------------------------------- if HAS_REPORTLAB: COLOR_PRIMARY = colors.HexColor("#1B4F72") COLOR_ACCENT = colors.HexColor("#E74C3C") COLOR_DARK = colors.HexColor("#2C3E50") COLOR_LIGHT_GRAY = colors.HexColor("#F2F3F4") COLOR_MEDIUM_GRAY = colors.HexColor("#BDC3C7") # --------------------------------------------------------------------------- # Style helpers # --------------------------------------------------------------------------- def _build_styles(): """Create custom paragraph styles for the report.""" styles = getSampleStyleSheet() styles.add(ParagraphStyle( "ReportTitle", parent=styles["Title"], fontSize=24, textColor=COLOR_PRIMARY, spaceAfter=6, alignment=TA_CENTER, fontName="Helvetica-Bold", )) styles.add(ParagraphStyle( "ReportSubtitle", parent=styles["Normal"], fontSize=12, textColor=COLOR_DARK, spaceAfter=20, alignment=TA_CENTER, fontName="Helvetica", )) styles.add(ParagraphStyle( "SectionHeading", parent=styles["Heading1"], fontSize=16, textColor=COLOR_PRIMARY, spaceBefore=18, spaceAfter=8, fontName="Helvetica-Bold", )) styles.add(ParagraphStyle( "SubHeading", parent=styles["Heading2"], fontSize=13, textColor=COLOR_DARK, spaceBefore=12, spaceAfter=6, fontName="Helvetica-Bold", )) styles.add(ParagraphStyle( "ReportBody", parent=styles["Normal"], fontSize=10, textColor=COLOR_DARK, spaceAfter=6, leading=14, fontName="Helvetica", )) styles.add(ParagraphStyle( "StatNumber", parent=styles["Normal"], fontSize=28, textColor=COLOR_ACCENT, alignment=TA_CENTER, spaceAfter=2, fontName="Helvetica-Bold", )) styles.add(ParagraphStyle( "StatLabel", parent=styles["Normal"], fontSize=9, textColor=COLOR_DARK, alignment=TA_CENTER, spaceAfter=8, fontName="Helvetica", )) styles.add(ParagraphStyle( "FigureCaption", parent=styles["Normal"], fontSize=9, textColor=COLOR_DARK, alignment=TA_CENTER, spaceAfter=12, fontName="Helvetica-Oblique", )) styles.add(ParagraphStyle( "FooterText", parent=styles["Normal"], fontSize=8, textColor=COLOR_MEDIUM_GRAY, alignment=TA_CENTER, fontName="Helvetica", )) return styles # --------------------------------------------------------------------------- # Component helpers # --------------------------------------------------------------------------- def _embed_figure(elements, image_path, caption, styles, max_width=6.0): """Embed a PNG figure if it exists, with caption.""" if not os.path.exists(image_path): elements.append(Paragraph( f"[Figure not available: {os.path.basename(image_path)}]", styles["ReportBody"], )) return img = Image(image_path) aspect = img.imageHeight / img.imageWidth img_width = min(max_width * inch, 6.5 * inch) img_height = img_width * aspect # Cap height to avoid overflow max_height = 5.0 * inch if img_height > max_height: img_height = max_height img_width = img_height / aspect img.drawWidth = img_width img.drawHeight = img_height elements.append(img) elements.append(Paragraph(caption, styles["FigureCaption"])) def _make_table(header, rows, col_widths=None): """Create a styled table with header row and alternating shading.""" data = [header] + rows style_commands = [ # Header ("BACKGROUND", (0, 0), (-1, 0), COLOR_PRIMARY), ("TEXTCOLOR", (0, 0), (-1, 0), colors.white), ("FONTNAME", (0, 0), (-1, 0), "Helvetica-Bold"), ("FONTSIZE", (0, 0), (-1, 0), 9), ("FONTNAME", (0, 1), (-1, -1), "Helvetica"), ("FONTSIZE", (0, 1), (-1, -1), 9), # Grid ("GRID", (0, 0), (-1, -1), 0.5, COLOR_MEDIUM_GRAY), ("VALIGN", (0, 0), (-1, -1), "MIDDLE"), ("TOPPADDING", (0, 0), (-1, -1), 4), ("BOTTOMPADDING", (0, 0), (-1, -1), 4), ("LEFTPADDING", (0, 0), (-1, -1), 6), ("RIGHTPADDING", (0, 0), (-1, -1), 6), ] # Alternating row shading for i in range(1, len(data)): if i % 2 == 0: style_commands.append( ("BACKGROUND", (0, i), (-1, i), COLOR_LIGHT_GRAY) ) tbl = Table(data, colWidths=col_widths, repeatRows=1) tbl.setStyle(TableStyle(style_commands)) return tbl def _find_figure(output_dir, filename): """Search for a figure in output_dir.""" path = os.path.join(output_dir, filename) return path # --------------------------------------------------------------------------- # Main report generator # --------------------------------------------------------------------------- def generate_report( results, experiments, synthesis, target="", disease="", output_dir=".", output_file=None, ): """ Generate a structured PDF analysis report for preclinical literature extraction. Parameters ---------- results : list Search results (paper dicts). experiments : list Experiment extraction dicts. synthesis : dict Synthesis summary from synthesize_preclinical(). target : str Molecular target name. disease : str Disease context. output_dir : str Directory containing figures and where PDF will be saved. output_file : str, optional Full path for output PDF. Defaults to output_dir/preclinical_report.pdf. Returns ------- str or None Path to generated PDF, or None if reportlab unavailable. """ if not HAS_REPORTLAB: print(" reportlab not installed - skipping PDF (markdown report available)") return None if output_file is None: output_file = os.path.join(output_dir, "preclinical_report.pdf") styles = _build_styles() elements = [] # Compute summary stats n_papers = synthesis.get("total_papers", len(experiments)) type_bd = synthesis.get("experiment_type_breakdown", {}) n_vitro = type_bd.get("in_vitro", 0) n_vivo = type_bd.get("in_vivo", 0) n_both = type_bd.get("both", 0) n_unclass = type_bd.get("unclassified", 0) n_cell_lines = len(synthesis.get("cell_line_frequency", {})) n_models = len(synthesis.get("animal_model_frequency", {})) # ===== TITLE PAGE ===== elements.append(Spacer(1, 0.8 * inch)) elements.append(Paragraph( "Preclinical Literature
Extraction Report", styles["ReportTitle"], )) elements.append(Paragraph( f"{target} in {disease}", styles["ReportSubtitle"], )) elements.append(Paragraph( datetime.now().strftime("%B %d, %Y"), styles["ReportSubtitle"], )) elements.append(Spacer(1, 0.3 * inch)) elements.append(HRFlowable( width="80%", thickness=1, color=COLOR_PRIMARY, spaceAfter=20, spaceBefore=10, )) # Summary stat boxes — row 1 stat_data = [ [Paragraph(f"{n_papers}", styles["StatNumber"]), Paragraph(f"{n_both}", styles["StatNumber"]), Paragraph(f"{n_cell_lines}", styles["StatNumber"])], [Paragraph("Papers Analyzed", styles["StatLabel"]), Paragraph("Both In Vitro + In Vivo", styles["StatLabel"]), Paragraph("Unique Cell Lines", styles["StatLabel"])], ] stat_table = Table(stat_data, colWidths=[2.2 * inch] * 3) stat_table.setStyle(TableStyle([ ("VALIGN", (0, 0), (-1, -1), "MIDDLE"), ("ALIGN", (0, 0), (-1, -1), "CENTER"), ])) elements.append(stat_table) elements.append(Spacer(1, 0.3 * inch)) # Summary stat boxes — row 2 stat_data2 = [ [Paragraph(f"{n_vitro}", styles["StatNumber"]), Paragraph(f"{n_vivo}", styles["StatNumber"]), Paragraph(f"{n_models}", styles["StatNumber"])], [Paragraph("In Vitro Only", styles["StatLabel"]), Paragraph("In Vivo Only", styles["StatLabel"]), Paragraph("Unique Animal Models", styles["StatLabel"])], ] stat_table2 = Table(stat_data2, colWidths=[2.2 * inch] * 3) stat_table2.setStyle(TableStyle([ ("VALIGN", (0, 0), (-1, -1), "MIDDLE"), ("ALIGN", (0, 0), (-1, -1), "CENTER"), ])) elements.append(stat_table2) # ===== 1. INTRODUCTION ===== elements.append(PageBreak()) elements.append(Paragraph("1. Introduction", styles["SectionHeading"])) elements.append(Paragraph( f"This report summarizes a systematic extraction of preclinical experiment details " f"from {n_papers} published studies investigating {target} in the context of " f"{disease}. The goal is to compile a structured overview of the preclinical " f"evidence landscape, including in vitro assays, in vivo models, and key findings.", styles["ReportBody"], )) elements.append(Paragraph( "Preclinical studies form the foundation of drug development, providing evidence " "of target engagement, efficacy in model systems, and safety signals before clinical " "testing. Understanding the breadth and consistency of preclinical evidence helps " "inform IND-enabling decisions and identify gaps in the evidence base.", styles["ReportBody"], )) elements.append(Paragraph( "Each paper was analyzed to extract structured details about: (1) in vitro experiments " "— cell lines used, assays performed, and key findings; and (2) in vivo experiments " "— animal models, endpoints measured, and outcomes. Results were synthesized across " "papers to identify common model systems, assay types, and concordance between in vitro " "and in vivo findings.", styles["ReportBody"], )) # ===== 2. METHODS ===== elements.append(Spacer(1, 0.2 * inch)) elements.append(Paragraph("2. Methods", styles["SectionHeading"])) elements.append(Paragraph("2.1 Literature Search", styles["SubHeading"])) elements.append(Paragraph( f"Papers were retrieved using the Consensus API (consensus.app), which performs " f"semantic search across indexed scientific literature. Multiple query variants " f"were used to maximize coverage: \"{target} {disease} preclinical\" and " f"\"{target} {disease} in vitro in vivo\". Results were deduplicated by DOI and " f"title, then sorted by publication date.", styles["ReportBody"], )) elements.append(Paragraph("2.2 Experiment Extraction", styles["SubHeading"])) elements.append(Paragraph( "Each abstract was parsed using keyword-based extraction to identify: " "(1) In vitro indicators — cell line names, assay types (viability, " "proliferation, apoptosis, migration, western blot, flow cytometry, etc.); " "(2) In vivo indicators — animal model types (xenograft, PDX, syngeneic, " "transgenic, orthotopic), endpoints (tumor growth, survival, pharmacokinetics, toxicity, " "histology, imaging); and (3) Key findings — quantitative results extracted " "from the abstract text.", styles["ReportBody"], )) elements.append(Paragraph("2.3 Synthesis", styles["SubHeading"])) elements.append(Paragraph( "Extracted experiments were aggregated across papers to compute frequency distributions " "for cell lines, assay types, animal models, and endpoints. Finding directions " "(positive, negative, neutral, combination) were classified based on keyword analysis. " "Evidence gaps were identified by checking for missing critical model types and endpoints.", styles["ReportBody"], )) # Parameters table elements.append(Paragraph("2.4 Search Parameters", styles["SubHeading"])) # Get year range from results years = [r.get("publication_date", "")[:4] for r in results if r.get("publication_date", "")] years = [y for y in years if y.isdigit()] year_range = f"{min(years)}–{max(years)}" if years else "N/A" param_rows = [ ["Target", target], ["Disease", disease], ["Search Backend", "Consensus API (consensus.app)"], ["Papers Retrieved", str(n_papers)], ["Publication Date Range", year_range], ] elements.append(_make_table( ["Parameter", "Value"], param_rows, col_widths=[2.5 * inch, 4.0 * inch], )) elements.append(Spacer(1, 0.1 * inch)) # ===== 3. RESULTS ===== elements.append(PageBreak()) elements.append(Paragraph("3. Results", styles["SectionHeading"])) # 3.1 Experiment type breakdown elements.append(Paragraph("3.1 Experiment Type Breakdown", styles["SubHeading"])) elements.append(Paragraph( f"Of {n_papers} papers analyzed, {n_both} reported both in vitro and in vivo " f"experiments, {n_vitro} reported in vitro only, {n_vivo} reported in vivo " f"only, and {n_unclass} could not be classified from abstract text alone.", styles["ReportBody"], )) type_rows = [ ["In vitro only", str(n_vitro), f"{n_vitro/max(n_papers,1)*100:.1f}%"], ["In vivo only", str(n_vivo), f"{n_vivo/max(n_papers,1)*100:.1f}%"], ["Both", str(n_both), f"{n_both/max(n_papers,1)*100:.1f}%"], ["Unclassified", str(n_unclass), f"{n_unclass/max(n_papers,1)*100:.1f}%"], ] elements.append(_make_table( ["Experiment Type", "Papers", "Percentage"], type_rows, col_widths=[2.5 * inch, 1.5 * inch, 1.5 * inch], )) elements.append(Spacer(1, 0.15 * inch)) # 3.2 Top cell lines cell_lines = synthesis.get("cell_line_frequency", {}) if cell_lines: elements.append(Paragraph("3.2 Most Common Cell Lines", styles["SubHeading"])) cl_rows = [[str(i), cl, str(count)] for i, (cl, count) in enumerate(list(cell_lines.items())[:10], 1)] elements.append(_make_table( ["Rank", "Cell Line", "Papers"], cl_rows, col_widths=[0.5 * inch, 3.0 * inch, 1.5 * inch], )) elements.append(Spacer(1, 0.15 * inch)) # 3.3 Top assay types assays = synthesis.get("assay_frequency", {}) if assays: elements.append(Paragraph("3.3 Most Common Assay Types", styles["SubHeading"])) assay_rows = [[str(i), assay, str(count)] for i, (assay, count) in enumerate(list(assays.items())[:10], 1)] elements.append(_make_table( ["Rank", "Assay Type", "Papers"], assay_rows, col_widths=[0.5 * inch, 3.0 * inch, 1.5 * inch], )) elements.append(Spacer(1, 0.15 * inch)) # 3.4 Animal models models = synthesis.get("animal_model_frequency", {}) if models: elements.append(Paragraph("3.4 Animal Model Types", styles["SubHeading"])) model_rows = [[str(i), model, str(count)] for i, (model, count) in enumerate(list(models.items())[:10], 1)] elements.append(_make_table( ["Rank", "Model Type", "Papers"], model_rows, col_widths=[0.5 * inch, 3.0 * inch, 1.5 * inch], )) elements.append(Spacer(1, 0.15 * inch)) # 3.5 Endpoints endpoints = synthesis.get("endpoint_frequency", {}) if endpoints: elements.append(Paragraph("3.5 Endpoints Measured", styles["SubHeading"])) ep_rows = [[str(i), ep, str(count)] for i, (ep, count) in enumerate(list(endpoints.items())[:10], 1)] elements.append(_make_table( ["Rank", "Endpoint", "Papers"], ep_rows, col_widths=[0.5 * inch, 3.0 * inch, 1.5 * inch], )) elements.append(Spacer(1, 0.15 * inch)) # 3.6 Visualizations (embedded PNG) elements.append(Paragraph("3.6 Visualizations", styles["SubHeading"])) plot_path = _find_figure(output_dir, "preclinical_plots.png") _embed_figure(elements, plot_path, "Figure 1. Four-panel preclinical experiment visualization: " "(A) Experiment type breakdown, (B) Top assay types, " "(C) Animal model distribution, (D) Publication timeline.", styles) elements.append(Spacer(1, 0.15 * inch)) # 3.7 Narrative synthesis (if available) narrative = synthesis.get("narrative", {}) if narrative: elements.append(Paragraph("3.7 Narrative Synthesis", styles["SubHeading"])) # Therapeutic direction td = narrative.get("therapeutic_direction", []) if td: elements.append(Paragraph( f"Therapeutic direction: {td[0]}", styles["ReportBody"], )) # Agreements agreements = narrative.get("agreements", []) if agreements: elements.append(Paragraph("Key agreements across studies:", styles["ReportBody"])) for a in agreements[:5]: elements.append(Paragraph(f"• {a}", styles["ReportBody"])) # Disagreements disagreements = narrative.get("disagreements", []) if disagreements: elements.append(Paragraph("Disagreements or inconsistencies:", styles["ReportBody"])) for d in disagreements[:5]: elements.append(Paragraph(f"• {d}", styles["ReportBody"])) # Gaps gaps = narrative.get("gaps", []) if gaps: elements.append(Paragraph("Evidence gaps:", styles["ReportBody"])) for g in gaps[:5]: elements.append(Paragraph(f"• {g}", styles["ReportBody"])) # ===== 4. CONCLUSIONS ===== elements.append(PageBreak()) elements.append(Paragraph("4. Conclusions", styles["SectionHeading"])) elements.append(Paragraph("4.1 Key Findings", styles["SubHeading"])) elements.append(Paragraph( f"This analysis extracted preclinical experiment details from {n_papers} papers " f"studying {target} in {disease}. ", styles["ReportBody"], )) if n_both > 0: elements.append(Paragraph( f"{n_both} papers reported both in vitro and in vivo experiments, " f"providing the strongest translational evidence. ", styles["ReportBody"], )) if cell_lines: top_cls = list(cell_lines.keys())[:3] elements.append(Paragraph( f"The most commonly used cell lines were {', '.join(top_cls)}.", styles["ReportBody"], )) if models: top_models = list(models.keys())[:3] elements.append(Paragraph( f"The most common animal model types were {', '.join(top_models)}.", styles["ReportBody"], )) elements.append(Paragraph("4.2 Caveats", styles["SubHeading"])) elements.append(Paragraph( "• Experiment extraction is based on abstract text only and may miss details " "available only in full-text methods sections.
" "• Keyword-based classification has inherent limitations — novel assay names " "or unconventional terminology may be missed.
" "• Publication bias may skew results toward positive findings.
" "• The search is limited to papers indexed by Consensus and may not cover " "all published preclinical studies.", styles["ReportBody"], )) elements.append(Paragraph("4.3 Suggested Next Steps", styles["SubHeading"])) elements.append(Paragraph( "• Full-text review of top papers (especially those with both in vitro " "and in vivo data) for detailed experimental parameters.
" "• Expand search with alternative target names or broader disease terms " "to capture additional studies.
" "• Functional enrichment analysis of target-related pathways using " "complementary bioinformatics skills.
" "• Clinical translation — compare preclinical findings with " "clinical trial outcomes using the clinicaltrials-landscape skill.", styles["ReportBody"], )) # Footer elements.append(Spacer(1, 0.5 * inch)) elements.append(HRFlowable( width="60%", thickness=0.5, color=COLOR_MEDIUM_GRAY, spaceAfter=8, spaceBefore=8, )) elements.append(Paragraph( f"Generated by literature-preclinical Agent Skill | " f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", styles["FooterText"], )) # ===== BUILD PDF ===== doc = SimpleDocTemplate( output_file, pagesize=letter, leftMargin=0.75 * inch, rightMargin=0.75 * inch, topMargin=0.75 * inch, bottomMargin=0.75 * inch, title=f"Preclinical Literature Extraction: {target} in {disease}", author="literature-preclinical Agent Skill", ) doc.build(elements) print(f" Saved: {output_file}") return output_file