mkdir -p /tmp/workspace/derm-biologics && cd /tmp/workspace/derm-biologics && python3 -c " import pdfplumber with pdfplumber.open('/tmp/workspace/attachments/54fbcb12-ea03-48a1-9c36-cc9085d24484/Biologics.pdf') as pdf: print('Pages:', len(pdf.pages)) for i, page in enumerate(pdf.pages): text = page.extract_text() or '' print(f'--- PAGE {i+1} ---') print(text) " > extracted.txt 2>&1 wc -l extracted.txt

"Figure 1" in ~/derm-biologics/extracted.txt

"mechanism of action" in ~/derm-biologics/extracted.txt

"Figure 2:" in ~/derm-biologics/extracted.txt

"mucous membrane pemphigoid" in ~/derm-biologics/extracted.txt

mkdir -p /tmp/workspace/derm-biologics/output && cd /tmp/workspace/derm-biologics && python3 << 'EOF' import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import matplotlib.patches as mpatches from matplotlib.patches import FancyBboxPatch, FancyArrowPatch import matplotlib.lines as mlines # ---------- DIAGRAM 1: PEMPHIGUS ---------- fig, ax = plt.subplots(figsize=(11, 14)) ax.set_xlim(0, 10) ax.set_ylim(0, 16) ax.axis('off') ax.set_title("Site of Action of Biologics in PEMPHIGUS", fontsize=16, fontweight='bold', pad=10) def box(x, y, w, h, text, fc, ec='black', fs=10, weight='normal'): b = FancyBboxPatch((x, y), w, h, boxstyle="round,pad=0.08", linewidth=1.4, edgecolor=ec, facecolor=fc) ax.add_patch(b) ax.text(x+w/2, y+h/2, text, ha='center', va='center', fontsize=fs, weight=weight, wrap=True) def arrow(x1,y1,x2,y2, color='black', style='-|>'): a = FancyArrowPatch((x1,y1),(x2,y2), arrowstyle=style, mutation_scale=18, color=color, linewidth=1.6) ax.add_patch(a) def drug_label(x, y, text, color): ax.annotate(text, xy=(x, y), fontsize=9.5, color=color, weight='bold', ha='left', va='center') # Main pathogenesis pipeline (center column) box(3.2, 14.4, 3.6, 1.0, "Naive B cell\n(pre-B -> mature B)", '#eaf4ff') box(3.2, 12.6, 3.6, 1.0, "B-cell activation via\nBCR signalling\n(BTK, PLC, NFkB, MAPK, PI3K)", '#eaf4ff') box(3.2, 10.8, 3.6, 1.0, "Autoreactive Plasma cell /\nPlasmablast (long-lived)", '#eaf4ff') box(3.2, 9.0, 3.6, 1.0, "Anti-Desmoglein (Dsg1/3)\nIgG autoantibody", '#fff6d8') box(3.2, 7.2, 3.6, 1.0, "Binding of IgG to\nDesmoglein on keratinocyte", '#fff6d8') box(3.2, 5.4, 3.6, 1.0, "Loss of cell-cell adhesion\n(ACANTHOLYSIS)", '#f0f0f0') box(3.2, 3.6, 3.6, 1.0, "Intraepidermal blister\n(Suprabasal / subcorneal)", '#e8e8e8') for (x1,y1,x2,y2) in [(5,14.4,5,13.6),(5,12.6,5,11.8),(5,10.8,5,10.0), (5,9.0,5,8.2),(5,7.2,5,6.4),(5,5.4,5,4.6)]: arrow(x1,y1,x2,y2) # Left side: B-cell/plasma cell targeting drugs (light green box per article) box(0.1, 13.6, 2.7, 1.6, "ANTI-CD20 mAb\nRituximab, Ofatumumab,\nVeltuzumab, Obinutuzumab", '#d9f2d0', fs=8.5) arrow(2.8,14.4,3.2,14.4, color='#2e7d32') box(0.1, 11.4, 2.7, 1.6, "ANTI-CD19 mAb\nInebilizumab,\nBlinatumomab\n(kills plasmablasts)", '#d9f2d0', fs=8.5) arrow(2.8,12.2,3.2,11.3, color='#2e7d32') box(0.1, 9.4, 2.7, 1.4, "BAFF inhibitor\nIanalumab (VAY736)\n-> B cell/plasma cell death", '#d9f2d0', fs=8.5) arrow(2.8,10.1,3.2,10.6, color='#2e7d32') box(0.1, 7.6, 2.7, 1.4, "BTK inhibitors\nIbrutinib, Rilzabrutinib\n-> block BCR signalling", '#d9f2d0', fs=8.5) arrow(2.8,8.3,3.2,13.0, color='#2e7d32') box(0.1, 5.7, 2.7, 1.3, "CAAR-T cells /\nDsg3-CAR-T\n-> selective B-cell kill", '#d9f2d0', fs=8.5) arrow(2.8,6.35,3.2,11.2, color='#2e7d32') # Right side: antibody half-life reduction (light yellow box) box(7.1, 8.4, 2.8, 1.6, "FcRn antagonists\nEfgartigimod,\nALXN1830 (SYNT001)\n-> reduce IgG half-life", '#fff2b0', fs=8.5) arrow(7.1,9.2,6.8,9.0, color='#b8860b') box(7.1, 6.4, 2.8, 1.6, "IVIg / Immunoadsorption\n-> neutralise & remove\npathogenic IgG", '#fff2b0', fs=8.5) arrow(7.1,7.2,6.8,7.7, color='#b8860b') # Cholinomimetics act at acantholysis level (gray box) box(7.1, 4.6, 2.8, 1.3, "Cholinomimetics\nPyridostigmine, Pilocarpine\n-> strengthen desmosomes,\nreduce acantholysis", '#e0e0e0', fs=8.5) arrow(7.1,5.25,6.8,5.9, color='#555555') # IL-4 inhibitor (dark yellow box) acting on inflammatory milieu box(7.1, 2.6, 2.8, 1.3, "IL-4 inhibitor\n(e.g. Dupilumab - trialled)\n-> reduce Th2 inflammatory\ncytokine support to B cells", '#e6c200', fs=8.5) arrow(7.1,3.25,6.8,4.0, color='#7a6100') # Legend ax.text(0.1, 1.6, "Legend:", fontsize=10, weight='bold') legend_items = [("Green = B cell / plasma cell modulation", '#d9f2d0'), ("Yellow = Reduces IgG half-life", '#fff2b0'), ("Gray = Acts at acantholysis level", '#e0e0e0'), ("Dark yellow = Interleukin inhibition", '#e6c200')] for i,(txt,c) in enumerate(legend_items): ax.add_patch(mpatches.Rectangle((0.1, 1.2-0.35*i), 0.3, 0.25, facecolor=c, edgecolor='black')) ax.text(0.5, 1.32-0.35*i, txt, fontsize=8.5, va='center') plt.tight_layout() plt.savefig('output/fig1_pemphigus.png', dpi=160, bbox_inches='tight') print("saved fig1") EOF

cd /tmp/workspace/derm-biologics && python3 << 'EOF' import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import matplotlib.patches as mpatches from matplotlib.patches import FancyBboxPatch, FancyArrowPatch fig, ax = plt.subplots(figsize=(11, 14)) ax.set_xlim(0, 10) ax.set_ylim(0, 16) ax.axis('off') ax.set_title("Site of Action of Biologics in BULLOUS PEMPHIGOID", fontsize=15, fontweight='bold', pad=10) def box(x, y, w, h, text, fc, ec='black', fs=9.5, weight='normal'): b = FancyBboxPatch((x, y), w, h, boxstyle="round,pad=0.08", linewidth=1.4, edgecolor=ec, facecolor=fc) ax.add_patch(b) ax.text(x+w/2, y+h/2, text, ha='center', va='center', fontsize=fs, weight=weight) def arrow(x1,y1,x2,y2, color='black'): a = FancyArrowPatch((x1,y1),(x2,y2), arrowstyle='-|>', mutation_scale=18, color=color, linewidth=1.6) ax.add_patch(a) # Central pathogenesis pipeline box(3.0, 14.4, 4.0, 1.0, "Autoreactive B cell", '#eaf4ff') box(3.0, 12.7, 4.0, 1.1, "Plasma cell -> Anti-BP180 (BPAG2) /\nAnti-BP230 (BPAG1) IgG + IgE", '#eaf4ff') box(3.0, 10.9, 4.0, 1.1, "Autoantibody binds hemidesmosome\nantigens at DEJ", '#fff6d8') box(3.0, 9.1, 4.0, 1.1, "Complement activation (C3, C5)\n+ Fc receptor engagement", '#f5d9d9') box(3.0, 7.3, 4.0, 1.1, "Th2 response: IL-4, IL-5, IL-13\nrelease; eosinophil chemotaxis\n(eotaxin), mast cell activation", '#e0f2e0') box(3.0, 5.5, 4.0, 1.1, "Neutrophil/eosinophil recruitment,\nrelease of MMP-9 & neutrophil\nelastase", '#f0f0f0') box(3.0, 3.7, 4.0, 1.0, "Dermo-epidermal separation\nSUBEPIDERMAL BLISTER", '#e8e8e8') for (x1,y1,x2,y2) in [(5,14.4,5,13.8),(5,12.7,5,12.0),(5,10.9,5,10.2), (5,9.1,5,8.4),(5,7.3,5,6.6),(5,5.5,5,4.7)]: arrow(x1,y1,x2,y2) # Left column drugs box(0.1, 13.6, 2.7, 1.4, "Anti-CD20 mAb\nRituximab\n-> B cell depletion", '#d9f2d0', fs=8.5) arrow(2.8,14.3,3.0,14.6, color='#2e7d32') box(0.1, 11.4, 2.7, 1.4, "Anti-IgE mAb\nOmalizumab, Ligelizumab\n-> blocks IgE-FceR binding", '#d9f2d0', fs=8.5) arrow(2.8,12.1,3.0,13.0, color='#2e7d32') box(0.1, 9.6, 2.7, 1.4, "Complement inhibitors\nAvdoralimab (anti-C5aR1),\nNomacopan (C5+LTB4),\nSutimlimab (anti-C1s)", '#f6c9c9', fs=8) arrow(2.8,10.3,3.0,9.6, color='#b03030') box(0.1, 7.6, 2.7, 1.5, "Anti-IL-4 mAb\nDupilumab\n-> blocks IL-4/IL-13,\nreduces B cell proliferation\n& eosinophil chemotaxis", '#c7e8c7', fs=8) arrow(2.8,8.35,3.0,7.9, color='#1b5e20') box(0.1, 5.7, 2.7, 1.5, "IL-5 inhibitors\nMepolizumab, Reslizumab,\nBenralizumab\n-> block eosinophil\nrecruitment/activation", '#c7e8c7', fs=8) arrow(2.8,6.45,3.0,6.5, color='#1b5e20') # Right column drugs box(7.2, 5.6, 2.8, 1.6, "Eotaxin-1 inhibitor\nBertilimumab\n-> reduces eosinophil\nmigration to skin", '#c7e8c7', fs=8.5) arrow(7.2,6.4,7.0,6.4, color='#1b5e20') box(7.2, 3.7, 2.8, 1.6, "IL-17/IL-23 inhibitors\nSecukinumab, Ixekizumab,\nUstekinumab, Tildrakizumab\n-> reduce MMP-9 &\nneutrophil elastase", '#e0e0e0', fs=8) arrow(7.2,4.5,7.0,4.7, color='#555555') # Legend ax.text(0.1, 2.6, "Legend:", fontsize=10, weight='bold') legend_items = [("Green = B cell / antibody / Th2-IL4 pathway", '#d9f2d0'), ("Pink = Complement pathway inhibition", '#f6c9c9'), ("Light green = IL-5 / eosinophil axis", '#c7e8c7'), ("Gray = IL-17/23, neutrophil-mediated tissue damage", '#e0e0e0')] for i,(txt,c) in enumerate(legend_items): ax.add_patch(mpatches.Rectangle((0.1, 2.2-0.35*i), 0.3, 0.25, facecolor=c, edgecolor='black')) ax.text(0.5, 2.32-0.35*i, txt, fontsize=8.5, va='center') plt.tight_layout() plt.savefig('output/fig2_bp.png', dpi=160, bbox_inches='tight') print("saved fig2") EOF

cd /tmp/workspace/derm-biologics && python3 << 'EOF' from docx import Document from docx.shared import Inches, Pt, RGBColor from docx.enum.text import WD_ALIGN_PARAGRAPH from docx.enum.table import WD_TABLE_ALIGNMENT doc = Document() def h1(text): p = doc.add_heading(text, level=1) return p def h2(text): p = doc.add_heading(text, level=2) return p def para(text, bold=False, italic=False, size=11): p = doc.add_paragraph() r = p.add_run(text) r.bold = bold r.italic = italic r.font.size = Pt(size) return p def bullet(text): p = doc.add_paragraph(text, style='List Bullet') return p doc.add_heading('BIOLOGICS IN AUTOIMMUNE BLISTERING DISORDERS', level=0) para('MD Dermatology - Quick Exam Notes | Source: Choudhary R, Gupta V, Khandpur S. "Updates on the Management of Autoimmune Bullous Diseases." Indian Dermatol Online J 2024;15:758-69.', italic=True, size=10) h1('1. Why Biologics? (Rationale)') for t in [ "Conventional therapy (systemic corticosteroids + steroid-sparing immunosuppressants) has broad, non-specific immunosuppressive action and long-term toxicity.", "Biologics/novel agents target specific steps in disease pathogenesis -> more precise, potentially safer, steroid-sparing therapy.", "Drugs are classified by SITE OF ACTION in the pathogenic cascade -- this is the key exam concept."]: bullet(t) h1('2. PEMPHIGUS (Intraepidermal blistering - anti-Desmoglein IgG)') para('Pathogenic cascade: B cell activation -> autoreactive plasma cell -> anti-Dsg1/Dsg3 IgG -> antibody binds desmoglein -> loss of keratinocyte adhesion (acantholysis) -> intraepidermal blister.', size=10) doc.add_picture('output/fig1_pemphigus.png', width=Inches(6.0)) doc.paragraphs[-1].alignment = WD_ALIGN_PARAGRAPH.CENTER para('Fig 1. Site of action of drugs in pemphigus (adapted from Choudhary et al., 2024)', italic=True, size=9) h2('2a. Drugs acting at B cell / plasma cell level') table = doc.add_table(rows=1, cols=4) table.style = 'Light Grid Accent 1' hdr = table.rows[0].cells for i, t in enumerate(['Class', 'Drug(s)', 'Mechanism', 'Key point / Evidence']): hdr[i].text = t for r in hdr[i].paragraphs[0].runs: r.bold = True rows_data = [ ["Anti-CD20 mAb", "Rituximab (1st line)\nOfatumumab, Veltuzumab,\nObinutuzumab, Tositumomab", "Antibody + complement-dependent B-cell lysis (Type I); Type II mAbs cause direct cell death/'tragocytosis'", "Rituximab = most effective steroid-sparing agent; recommended 1st-line in moderate-severe PV. CD20 absent on plasma cells & pro-B cells -> relapse possible via B-cell repopulation"], ["Anti-CD19 mAb", "Inebilizumab, Blinatumomab", "Kills long-lived plasmablasts producing anti-Dsg IgG", "Not yet tried in pemphigus (theoretical)"], ["BAFF inhibitor", "Ianalumab (VAY736)", "Blocks B-cell activating factor -> eliminates autoreactive B cells, reduces plasma cell longevity", "Phase 2 PV trial terminated early but showed dose-dependent fall in PDAI/ABSIS scores"], ["BTK inhibitors", "Ibrutinib (irreversible)\nRilzabrutinib (reversible, oral)", "Block Bruton tyrosine kinase in BCR signalling pathway -> reduced antibody & cytokine production", "Rilzabrutinib better safety profile, no B-cell depletion; Phase 3 trial did not beat placebo"], ["CAAR-T / CAR-T cells", "Dsg3-CAR-T, Tisagenlecleucel", "Chimeric antigen receptor T-cells selectively target and kill anti-Dsg B cells", "Phase 1 trial for mucosal PV ongoing (expected 2026)"], ] for row in rows_data: cells = table.add_row().cells for i, val in enumerate(row): cells[i].text = val h2('2b. Drugs reducing autoantibody half-life') bullet("FcRn antagonists - Efgartigimod, ALXN1830 (SYNT001): block neonatal Fc receptor (FcRn) that normally recycles IgG -> increases IgG degradation -> rapid fall in pathogenic anti-Dsg antibodies. Efgartigimod showed disease control in 90% within median 17 days.") bullet("IVIg: neutralises pathogenic antibodies, saturates FcRn (competitively blocks IgG recycling), suppresses antibody production, modulates immune system.") bullet("Immunoadsorption: physically removes circulating IgG; combined with best medical treatment reduces cumulative steroid dose but more serious adverse effects.") h2('2c. Drug acting at level of acantholysis') bullet("Cholinomimetics - Oral pyridostigmine, topical pilocarpine: strengthen desmosomal adhesion / counter acetylcholine-related loss of keratinocyte adhesion -> reduce acantholysis. Limited evidence, small open-label studies.") h2('2d. Interleukin inhibition') bullet("IL-4 inhibitor (e.g. dupilumab, being explored): dampens Th2-driven inflammatory support to B cells.") h1('3. BULLOUS PEMPHIGOID (Subepidermal blistering - anti-BP180/BP230)') para('Pathogenic cascade: Autoreactive B cell -> plasma cell -> anti-BP180 (BPAG2)/anti-BP230 (BPAG1) IgG & IgE -> antibody binds hemidesmosomal antigen at dermo-epidermal junction -> complement activation (C3, C5) + Fc receptor engagement -> Th2 response (IL-4, IL-5, IL-13) with eosinophil chemotaxis & mast cell activation -> neutrophil/eosinophil recruitment, release of MMP-9 & neutrophil elastase -> dermo-epidermal separation -> subepidermal blister.', size=10) doc.add_picture('output/fig2_bp.png', width=Inches(6.0)) doc.paragraphs[-1].alignment = WD_ALIGN_PARAGRAPH.CENTER para('Fig 2. Site of action of drugs in bullous pemphigoid (adapted from Choudhary et al., 2024)', italic=True, size=9) table2 = doc.add_table(rows=1, cols=4) table2.style = 'Light Grid Accent 1' hdr2 = table2.rows[0].cells for i, t in enumerate(['Class', 'Drug(s)', 'Mechanism', 'Key point / Evidence']): hdr2[i].text = t for r in hdr2[i].paragraphs[0].runs: r.bold = True rows2 = [ ["Anti-CD20 mAb", "Rituximab", "B-cell depletion (antibody + complement-dependent lysis)", "No RCTs; case series: 70.5% complete remission. Included as 3rd-line in EADV S2K guidelines"], ["Anti-IgE mAb", "Omalizumab (most studied)\nLigelizumab", "Blocks IgE binding to FceRI -> reduced Th2/mast cell activation", "Omalizumab: case reports/series show improvement (approved for asthma/urticaria, repurposed). Ligelizumab failed phase 2 primary endpoint"], ["Anti-IL-4 mAb", "Dupilumab", "Blocks IL-4/IL-13 receptor -> down-regulates B-cell proliferation, eosinophil chemotaxis, Th2 chemokines", "Case reports/series show improvement; increasingly used off-label in recalcitrant BP"], ["Complement inhibitors", "Avdoralimab (anti-C5aR1)\nNomacopan (anti-C5 + anti-LTB4)\nSutimlimab (anti-C1s)", "Blocks complement cascade -> reduced granulocyte migration & inflammation", "Nomacopan: positive Phase 2, Phase 3 ongoing. Sutimlimab has FDA orphan drug status for BP"], ["IL-5 inhibitors", "Mepolizumab, Reslizumab, Benralizumab", "Block eosinophil recruitment/activation/survival", "Reslizumab: excellent (case reports). Mepolizumab failed RCT primary endpoint. Benralizumab in ongoing phase 3"], ["Eotaxin-1 inhibitor", "Bertilimumab", "Blocks eotaxin-1 -> reduced eosinophil migration to skin", "Phase 2 open-label promising; double-blind RCT results awaited"], ["IL-17 / IL-23 inhibitors", "Secukinumab, Ixekizumab,\nUstekinumab, Tildrakizumab", "Reduce secretion of MMP-9 and neutrophil elastase", "Mixed results - secukinumab case report improvement; ixekizumab failed phase 2"], ] for row in rows2: cells = table2.add_row().cells for i, val in enumerate(row): cells[i].text = val h1('4. Exam Quick-Recall Table (Drug -> Target -> One-liner)') table3 = doc.add_table(rows=1, cols=3) table3.style = 'Light Grid Accent 1' h3 = table3.rows[0].cells for i,t in enumerate(['Drug','Target','One-liner']): h3[i].text = t for r in h3[i].paragraphs[0].runs: r.bold = True quick = [ ["Rituximab","CD20 on B cells","1st line steroid-sparing biologic in PV (both pemphigus & BP)"], ["Efgartigimod","FcRn","Rapid antibody clearance; fast disease control in PV/PF"], ["Rilzabrutinib","BTK","Oral, no B-cell depletion; safer than ibrutinib"], ["Ianalumab (VAY736)","BAFF","Kills autoreactive B cells & shortens plasma cell survival"], ["Omalizumab","IgE - FceRI","Repurposed anti-asthma drug for BP"], ["Dupilumab","IL-4/IL-13R","Blocks Th2 axis; used in both PV (trial) and BP"], ["Nomacopan","C5 + LTB4","Dual complement/leukotriene block, promising in BP"], ["Bertilimumab","Eotaxin-1","Reduces eosinophil skin infiltration in BP"], ["Pyridostigmine/Pilocarpine","Cholinergic pathway","Reduces acantholysis directly (non-immune site)"], ] for row in quick: cells = table3.add_row().cells for i,val in enumerate(row): cells[i].text = val h1('5. High-Yield One-Liners for Viva') for t in [ "Rituximab is the only biologic recommended as FIRST-LINE in moderate-to-severe pemphigus vulgaris.", "Efgartigimod (FcRn antagonist) achieves the FASTEST disease control among emerging pemphigus drugs.", "CD20 is absent on plasma cells and pro-B cells -> explains relapse after rituximab (from B-cell repopulation).", "No biologic is yet FDA-approved for bullous pemphigoid; all current biologic use in BP is off-label/repurposed.", "BP pathogenesis has a dual antibody-mediated (complement) AND Th2/eosinophil-mediated arm -> two separate drug target groups.", "Sutimlimab (anti-C1s) has FDA orphan drug status specifically for BP.", "Cholinomimetics are unique - they act directly at the acantholysis step, not on the immune system."]: bullet(t) doc.save('output/Biologics_in_AIBD_Exam_Notes.docx') print("saved docx") EOF ls -la /tmp/workspace/derm-biologics/output/