Pharmacology Notes

mkdir -p /home/daytona/workspace/pharmacokinetics-guide && ls /home/daytona/work…

~/pharmacokinetics-guide/generate_pk_guide.py

from reportlab.lib.pagesizes import A4
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm
from reportlab.lib import colors
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    HRFlowable, PageBreak, KeepTogether
)
from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_JUSTIFY
from reportlab.platypus import ListFlowable, ListItem

OUTPUT_PATH = "/home/daytona/workspace/pharmacokinetics-guide/Pharmacokinetics_Study_Guide.pdf"

# ── Color Palette ──────────────────────────────────────────────────────────
NAVY    = colors.HexColor("#1a3a5c")
TEAL    = colors.HexColor("#0d6e8a")
LIGHT_TEAL = colors.HexColor("#e8f4f8")
ACCENT  = colors.HexColor("#e8a020")
LIGHT_GRAY = colors.HexColor("#f5f5f5")
MED_GRAY   = colors.HexColor("#cccccc")
WHITE   = colors.white
RED_BOX = colors.HexColor("#fff3f3")
RED_BORDER = colors.HexColor("#c0392b")
GREEN_BOX = colors.HexColor("#f0fff4")
GREEN_BORDER = colors.HexColor("#27ae60")

doc = SimpleDocTemplate(
    OUTPUT_PATH,
    pagesize=A4,
    leftMargin=2*cm, rightMargin=2*cm,
    topMargin=2.5*cm, bottomMargin=2.5*cm,
    title="Pharmacokinetics Study Guide",
    author="Orris Medical Education",
)

styles = getSampleStyleSheet()

# Custom styles
cover_title = ParagraphStyle("CoverTitle", parent=styles["Title"],
    fontSize=32, textColor=WHITE, alignment=TA_CENTER, spaceAfter=6,
    fontName="Helvetica-Bold", leading=38)

cover_sub = ParagraphStyle("CoverSub", parent=styles["Normal"],
    fontSize=14, textColor=colors.HexColor("#d0eaf5"), alignment=TA_CENTER,
    fontName="Helvetica", spaceAfter=4)

chapter_heading = ParagraphStyle("ChapterHeading",
    fontSize=18, textColor=WHITE, fontName="Helvetica-Bold",
    alignment=TA_LEFT, spaceAfter=4, spaceBefore=8, leading=22,
    backColor=NAVY, borderPad=(8, 8, 8, 8))

section_heading = ParagraphStyle("SectionHeading",
    fontSize=13, textColor=NAVY, fontName="Helvetica-Bold",
    spaceBefore=12, spaceAfter=4, leading=16, borderPad=4)

sub_heading = ParagraphStyle("SubHeading",
    fontSize=11, textColor=TEAL, fontName="Helvetica-Bold",
    spaceBefore=8, spaceAfter=3, leading=14)

body = ParagraphStyle("Body", parent=styles["Normal"],
    fontSize=10, leading=15, spaceAfter=5, alignment=TA_JUSTIFY,
    fontName="Helvetica")

bullet_style = ParagraphStyle("Bullet", parent=body,
    leftIndent=16, bulletIndent=4, spaceAfter=3, leading=14)

formula_style = ParagraphStyle("Formula",
    fontSize=11, fontName="Helvetica-Bold", textColor=NAVY,
    alignment=TA_CENTER, spaceBefore=6, spaceAfter=6,
    backColor=LIGHT_TEAL, borderPad=8, leading=16)

callout_style = ParagraphStyle("Callout",
    fontSize=9.5, fontName="Helvetica", leading=14,
    textColor=colors.HexColor("#333333"), spaceAfter=4)

tip_title = ParagraphStyle("TipTitle",
    fontSize=10, fontName="Helvetica-Bold", textColor=GREEN_BORDER,
    spaceAfter=2, leading=13)

warn_title = ParagraphStyle("WarnTitle",
    fontSize=10, fontName="Helvetica-Bold", textColor=RED_BORDER,
    spaceAfter=2, leading=13)

story = []

# ═══════════════════════════════════════════════════════════════════
# COVER PAGE
# ═══════════════════════════════════════════════════════════════════
cover_table = Table(
    [[Paragraph("PHARMACOKINETICS", cover_title),
      Paragraph("A Complete Study Guide for Medical Students", cover_sub),
      Paragraph("MBBS · MD · Pharmacology", cover_sub),
      Spacer(1, 0.5*cm),
      Paragraph("Based on: Lippincott Illustrated Reviews: Pharmacology · Goodman & Gilman's · Katzung's", 
                ParagraphStyle("CoverRef", parent=cover_sub, fontSize=9, textColor=colors.HexColor("#a0c8e0"))),
    ]],
    colWidths=[17*cm],
    rowHeights=[None]
)
cover_table.setStyle(TableStyle([
    ("BACKGROUND", (0,0), (-1,-1), NAVY),
    ("TOPPADDING", (0,0), (-1,-1), 60),
    ("BOTTOMPADDING", (0,0), (-1,-1), 60),
    ("LEFTPADDING", (0,0), (-1,-1), 30),
    ("RIGHTPADDING", (0,0), (-1,-1), 30),
    ("VALIGN", (0,0), (-1,-1), "MIDDLE"),
]))
story.append(cover_table)

# Decorative accent bar
story.append(Table([[""]], colWidths=[17*cm], rowHeights=[0.4*cm],
    style=[("BACKGROUND",(0,0),(-1,-1), ACCENT)]))
story.append(Spacer(1, 0.3*cm))

def section_divider(title):
    t = Table([[Paragraph(f"  {title}", chapter_heading)]], colWidths=[17*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND",(0,0),(-1,-1), NAVY),
        ("TOPPADDING",(0,0),(-1,-1),8),
        ("BOTTOMPADDING",(0,0),(-1,-1),8),
        ("LEFTPADDING",(0,0),(-1,-1),12),
    ]))
    return t

def callout_box(title, items, bg=LIGHT_TEAL, border=TEAL, title_color=TEAL):
    title_p = Paragraph(title, ParagraphStyle("CBTitle", fontSize=10,
        fontName="Helvetica-Bold", textColor=title_color, spaceAfter=3))
    content = [title_p]
    for item in items:
        content.append(Paragraph(f"• {item}", callout_style))
    t = Table([[content]], colWidths=[16*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND",(0,0),(-1,-1), bg),
        ("BOX",(0,0),(-1,-1), 1, border),
        ("TOPPADDING",(0,0),(-1,-1),8),
        ("BOTTOMPADDING",(0,0),(-1,-1),8),
        ("LEFTPADDING",(0,0),(-1,-1),12),
        ("RIGHTPADDING",(0,0),(-1,-1),8),
    ]))
    return t

def formula_box(formula_text, label=None):
    items = []
    if label:
        items.append(Paragraph(label, ParagraphStyle("FLabel", fontSize=9,
            fontName="Helvetica", textColor=TEAL, spaceAfter=2, alignment=TA_CENTER)))
    items.append(Paragraph(formula_text, formula_style))
    t = Table([[ items ]], colWidths=[16*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND",(0,0),(-1,-1), LIGHT_TEAL),
        ("BOX",(0,0),(-1,-1), 1.5, TEAL),
        ("TOPPADDING",(0,0),(-1,-1),10),
        ("BOTTOMPADDING",(0,0),(-1,-1),10),
        ("LEFTPADDING",(0,0),(-1,-1),10),
        ("RIGHTPADDING",(0,0),(-1,-1),10),
    ]))
    return t

# ═══════════════════════════════════════════════════════════════════
# CONTENTS PAGE
# ═══════════════════════════════════════════════════════════════════
story.append(PageBreak())
story.append(Paragraph("Contents", ParagraphStyle("TOCTitle", fontSize=20,
    fontName="Helvetica-Bold", textColor=NAVY, spaceAfter=16, spaceBefore=4)))
story.append(HRFlowable(width="100%", thickness=2, color=TEAL, spaceAfter=10))

toc_items = [
    ("1", "Overview of Pharmacokinetics"),
    ("2", "Absorption"),
    ("3", "Bioavailability & First-Pass Metabolism"),
    ("4", "Distribution"),
    ("5", "Metabolism (Biotransformation)"),
    ("6", "Elimination & Renal Excretion"),
    ("7", "Key Pharmacokinetic Parameters"),
    ("8", "Half-Life & Steady State"),
    ("9", "Dosage Regimens"),
    ("10", "Special Populations"),
    ("11", "Quick-Reference Formulas"),
    ("12", "MCQ-Style Revision Quiz"),
]
toc_data = []
for num, title in toc_items:
    toc_data.append([
        Paragraph(f"<b>{num}.</b>", ParagraphStyle("TOCNum", fontSize=10,
            fontName="Helvetica-Bold", textColor=TEAL)),
        Paragraph(title, ParagraphStyle("TOCItem", fontSize=10,
            fontName="Helvetica", textColor=colors.HexColor("#222"))),
    ])
toc_table = Table(toc_data, colWidths=[1.2*cm, 15*cm])
toc_table.setStyle(TableStyle([
    ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
    ("TOPPADDING",(0,0),(-1,-1),4),
    ("BOTTOMPADDING",(0,0),(-1,-1),4),
    ("LINEBELOW",(0,0),(-1,-1),0.3,MED_GRAY),
]))
story.append(toc_table)

# ═══════════════════════════════════════════════════════════════════
# SECTION 1 – OVERVIEW
# ═══════════════════════════════════════════════════════════════════
story.append(PageBreak())
story.append(section_divider("1  Overview of Pharmacokinetics"))
story.append(Spacer(1, 0.3*cm))
story.append(Paragraph(
    "<b>Pharmacokinetics (PK)</b> is the study of <i>what the body does to a drug</i>. "
    "It encompasses the processes of <b>Absorption, Distribution, Metabolism, and Elimination (ADME)</b>. "
    "Together, these four processes determine the onset, intensity, and duration of drug action.",
    body))
story.append(Spacer(1, 0.2*cm))
story.append(callout_box("ADME at a Glance", [
    "Absorption – drug enters the systemic circulation from the site of administration",
    "Distribution – drug reversibly leaves the bloodstream into interstitial and intracellular fluids",
    "Metabolism – biotransformation by the liver (mainly) or other tissues",
    "Elimination – removal from the body via urine, bile, or feces",
], bg=LIGHT_TEAL, border=TEAL))
story.append(Spacer(1, 0.2*cm))
story.append(Paragraph(
    "Clinicians use PK parameters to design optimal drug regimens – choosing the correct "
    "<b>route of administration, dose, frequency, and duration</b> of treatment.",
    body))

# ═══════════════════════════════════════════════════════════════════
# SECTION 2 – ABSORPTION
# ═══════════════════════════════════════════════════════════════════
story.append(PageBreak())
story.append(section_divider("2  Absorption"))
story.append(Spacer(1, 0.3*cm))
story.append(Paragraph(
    "Absorption is the movement of a drug from its site of administration into the systemic circulation. "
    "The rate and extent of absorption depend on the drug's physicochemical properties and the route used.",
    body))

story.append(Paragraph("2.1  Routes of Administration", section_heading))
routes_data = [
    [Paragraph("<b>Route</b>", callout_style), Paragraph("<b>Onset</b>", callout_style),
     Paragraph("<b>Key Features</b>", callout_style), Paragraph("<b>Examples</b>", callout_style)],
    ["Oral (PO)", "Slow–Moderate", "Convenient; subject to first-pass metabolism; GI absorption", "Most tablets, capsules"],
    ["Sublingual/Buccal", "Fast", "Bypasses first-pass; absorbed via oral mucosa", "Nitroglycerine, Buprenorphine"],
    ["Intravenous (IV)", "Immediate", "100% bioavailability; no absorption step", "Antibiotics, chemotherapy"],
    ["Intramuscular (IM)", "Moderate", "Good for depot preparations; bypasses GI", "Vaccines, Haloperidol decanoate"],
    ["Subcutaneous (SC)", "Moderate", "Slower than IM; suitable for proteins/insulins", "Insulin, Heparin"],
    ["Transdermal", "Very slow", "Sustained release; bypasses first-pass", "Nicotine patch, Fentanyl patch"],
    ["Inhalation", "Fast", "Large surface area; direct lung delivery", "Salbutamol, Anaesthetic gases"],
    ["Rectal", "Variable", "Partial first-pass bypass; used in vomiting/children", "Diazepam rectal gel"],
    ["Intrathecal", "Immediate (CNS)", "Bypasses blood-brain barrier directly", "Spinal anaesthetics, Methotrexate"],
]
rt = Table(routes_data, colWidths=[3*cm, 2.4*cm, 6.5*cm, 5.1*cm])
rt.setStyle(TableStyle([
    ("BACKGROUND",(0,0),(-1,0), NAVY),
    ("TEXTCOLOR",(0,0),(-1,0), WHITE),
    ("FONTNAME",(0,0),(-1,0),"Helvetica-Bold"),
    ("FONTSIZE",(0,0),(-1,-1),9),
    ("ROWBACKGROUNDS",(0,1),(-1,-1),[WHITE, LIGHT_GRAY]),
    ("GRID",(0,0),(-1,-1),0.4, MED_GRAY),
    ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
    ("TOPPADDING",(0,0),(-1,-1),5),
    ("BOTTOMPADDING",(0,0),(-1,-1),5),
    ("LEFTPADDING",(0,0),(-1,-1),5),
]))
story.append(rt)

story.append(Paragraph("2.2  Mechanisms of Drug Absorption", section_heading))
mech_data = [
    ["Passive Diffusion", "Most common. Drug moves down concentration gradient (high to low). Lipid-soluble drugs cross membranes easily. No energy required."],
    ["Facilitated Diffusion", "Uses carrier protein (transporter). Down concentration gradient. No energy. E.g., glucose transport."],
    ["Active Transport", "Requires energy (ATP). Against concentration gradient. Saturable and inhibitable. E.g., P-glycoprotein efflux."],
    ["Endocytosis/Pinocytosis", "Cell engulfs drug. Important for large molecules (proteins, vaccines)."],
    ["Filtration", "Water-soluble small molecules pass through pores/slit junctions (e.g., capillary endothelium)."],
]
mt = Table(mech_data, colWidths=[4.5*cm, 12.5*cm])
mt.setStyle(TableStyle([
    ("BACKGROUND",(0,0),(0,-1), LIGHT_TEAL),
    ("FONTNAME",(0,0),(0,-1),"Helvetica-Bold"),
    ("FONTSIZE",(0,0),(-1,-1),9.5),
    ("GRID",(0,0),(-1,-1),0.4,MED_GRAY),
    ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
    ("TOPPADDING",(0,0),(-1,-1),6),
    ("BOTTOMPADDING",(0,0),(-1,-1),6),
    ("LEFTPADDING",(0,0),(-1,-1),6),
]))
story.append(mt)
story.append(Spacer(1, 0.3*cm))
story.append(callout_box("Henderson-Hasselbalch Principle",
    ["Weak acids (pKa 3-5): mostly un-ionized in stomach (acidic) → better absorbed from stomach",
     "Weak bases (pKa 8-10): mostly un-ionized in small intestine (alkaline) → better absorbed from intestine",
     "Un-ionized form is lipid-soluble and crosses membranes; ionized form is water-soluble and trapped",
     "Ion trapping: acidic drugs accumulate in alkaline compartments (e.g., basic urine) and vice versa"],
    bg=LIGHT_TEAL, border=TEAL))

# ═══════════════════════════════════════════════════════════════════
# SECTION 3 – BIOAVAILABILITY
# ═══════════════════════════════════════════════════════════════════
story.append(PageBreak())
story.append(section_divider("3  Bioavailability & First-Pass Metabolism"))
story.append(Spacer(1, 0.3*cm))
story.append(Paragraph(
    "<b>Bioavailability (F)</b> is the fraction of an administered dose that reaches the systemic circulation "
    "in an unchanged (active) form. IV administration gives 100% bioavailability by definition. "
    "Oral bioavailability is almost always less than 100% due to incomplete absorption and first-pass metabolism.",
    body))
story.append(Spacer(1, 0.2*cm))
story.append(formula_box(
    "F  =  AUC<sub>oral</sub>  /  AUC<sub>IV</sub>  ×  100%",
    "Bioavailability Formula (AUC = Area Under the Plasma Concentration-Time Curve)"))
story.append(Spacer(1, 0.3*cm))

story.append(Paragraph("3.1  First-Pass (Presystemic) Metabolism", section_heading))
story.append(Paragraph(
    "After oral ingestion, a drug is absorbed from the GI tract and transported to the liver via the portal vein "
    "<i>before</i> reaching the systemic circulation. Hepatic enzymes (mainly CYP450) may metabolize a significant "
    "fraction of the drug, reducing the amount available. This is called the <b>first-pass effect</b> "
    "or <b>presystemic elimination</b>.",
    body))
story.append(Spacer(1, 0.2*cm))
fp_data = [
    [Paragraph("<b>High First-Pass Drugs</b>", callout_style), Paragraph("<b>Strategy to Bypass</b>", callout_style)],
    ["Morphine, Meperidine", "Use IV or IM route"],
    ["Midazolam, Nifedipine", "Sublingual / IV"],
    ["Nitroglycerin", "Sublingual or transdermal patch"],
    ["Lidocaine", "IV (not oral)"],
    ["Propranolol", "Oral dose must be much higher than IV dose"],
    ["Estradiol", "Transdermal patch, vaginal preparation"],
]
fpt = Table(fp_data, colWidths=[8.5*cm, 8.5*cm])
fpt.setStyle(TableStyle([
    ("BACKGROUND",(0,0),(-1,0), TEAL),
    ("TEXTCOLOR",(0,0),(-1,0), WHITE),
    ("FONTNAME",(0,0),(-1,0),"Helvetica-Bold"),
    ("FONTSIZE",(0,0),(-1,-1),9.5),
    ("ROWBACKGROUNDS",(0,1),(-1,-1),[WHITE, LIGHT_GRAY]),
    ("GRID",(0,0),(-1,-1),0.4,MED_GRAY),
    ("TOPPADDING",(0,0),(-1,-1),5),
    ("BOTTOMPADDING",(0,0),(-1,-1),5),
    ("LEFTPADDING",(0,0),(-1,-1),8),
    ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
]))
story.append(fpt)
story.append(Spacer(1, 0.3*cm))
story.append(callout_box("Factors Affecting Bioavailability",
    ["Drug solubility and particle size (dissolution step must occur first)",
     "Gut wall metabolism by CYP3A4 (e.g., reduces midazolam bioavailability)",
     "Efflux transporters: P-glycoprotein pumps drug back into gut lumen",
     "GI pH and motility – affect dissolution and absorption rate",
     "Food interactions – high-fat meals may increase or decrease absorption"],
    bg=LIGHT_TEAL, border=TEAL))

# ═══════════════════════════════════════════════════════════════════
# SECTION 4 – DISTRIBUTION
# ═══════════════════════════════════════════════════════════════════
story.append(PageBreak())
story.append(section_divider("4  Distribution"))
story.append(Spacer(1, 0.3*cm))
story.append(Paragraph(
    "After absorption, drugs distribute from the plasma into the interstitial and intracellular fluids. "
    "The extent of distribution depends on <b>lipophilicity, plasma protein binding, tissue binding, "
    "and blood flow</b> to organs.",
    body))

story.append(Paragraph("4.1  Volume of Distribution (Vd)", section_heading))
story.append(Paragraph(
    "The <b>apparent volume of distribution (Vd)</b> is the theoretical volume that would be required "
    "to contain the total body drug at the same concentration as measured in plasma. "
    "It does not correspond to a real physiological volume.",
    body))
story.append(formula_box(
    "Vd  =  Dose / C₀  (liters)",
    "Vd = Amount of drug in body ÷ Plasma drug concentration at time zero (C₀)"))
story.append(Spacer(1, 0.3*cm))

vd_data = [
    [Paragraph("<b>Vd (approx.)</b>", callout_style), Paragraph("<b>Compartment</b>", callout_style),
     Paragraph("<b>Drug Type</b>", callout_style), Paragraph("<b>Example</b>", callout_style)],
    ["~4 L", "Plasma (vascular)", "High MW or extensively protein-bound", "Heparin, Warfarin"],
    ["~14 L", "Extracellular fluid", "Low MW, hydrophilic", "Aminoglycosides, Mannitol"],
    ["~42 L", "Total body water", "Low MW, moderately lipophilic", "Ethanol, Many small drugs"],
    [">100 L", "Tissue sequestration", "Highly lipophilic / tissue-bound", "Chloroquine, Amiodarone"],
]
vdt = Table(vd_data, colWidths=[2.5*cm, 4.5*cm, 5.5*cm, 4.5*cm])
vdt.setStyle(TableStyle([
    ("BACKGROUND",(0,0),(-1,0), NAVY),
    ("TEXTCOLOR",(0,0),(-1,0), WHITE),
    ("FONTNAME",(0,0),(-1,0),"Helvetica-Bold"),
    ("FONTSIZE",(0,0),(-1,-1),9.5),
    ("ROWBACKGROUNDS",(0,1),(-1,-1),[WHITE, LIGHT_GRAY]),
    ("GRID",(0,0),(-1,-1),0.4,MED_GRAY),
    ("TOPPADDING",(0,0),(-1,-1),6),
    ("BOTTOMPADDING",(0,0),(-1,-1),6),
    ("LEFTPADDING",(0,0),(-1,-1),6),
    ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
]))
story.append(vdt)
story.append(Spacer(1, 0.3*cm))

story.append(Paragraph("4.2  Plasma Protein Binding", section_heading))
story.append(Paragraph(
    "Many drugs bind reversibly to plasma proteins (mainly <b>albumin</b> for acidic drugs; "
    "<b>alpha-1-acid glycoprotein</b> for basic drugs). Only the <b>free (unbound)</b> fraction "
    "is pharmacologically active and can cross membranes, reach target tissues, be metabolized, "
    "or be excreted.",
    body))
story.append(callout_box("Protein Binding – Key Points",
    ["Highly protein-bound drugs (>90%): Warfarin, Phenytoin, NSAIDs",
     "Drug displacement interactions: two highly bound drugs compete for binding sites – free concentration rises → risk of toxicity",
     "Hypoalbuminaemia (e.g., liver disease, malnutrition) → increased free drug fraction",
     "Large Vd = drug has left plasma and concentrated in tissues",
     "Small Vd = drug remains mostly in plasma (e.g., highly protein-bound)"],
    bg=LIGHT_TEAL, border=TEAL))

story.append(Paragraph("4.3  Blood-Brain Barrier (BBB)", section_heading))
story.append(Paragraph(
    "The BBB consists of tight junctions between brain capillary endothelial cells. "
    "Only <b>lipophilic, un-ionized, and low-molecular-weight</b> drugs can cross it. "
    "P-glycoprotein efflux transporters further limit CNS penetration. "
    "Inflammation (e.g., meningitis) can transiently disrupt the BBB.",
    body))

# ═══════════════════════════════════════════════════════════════════
# SECTION 5 – METABOLISM
# ═══════════════════════════════════════════════════════════════════
story.append(PageBreak())
story.append(section_divider("5  Metabolism (Biotransformation)"))
story.append(Spacer(1, 0.3*cm))
story.append(Paragraph(
    "Drug metabolism converts lipophilic drugs into more polar (water-soluble) metabolites, "
    "making them easier to excrete. The <b>liver</b> is the primary site, though the gut wall, "
    "lungs, kidneys, and plasma also contribute. Metabolism may <b>inactivate, activate, or alter</b> drug activity.",
    body))

story.append(Paragraph("5.1  Phase I Reactions", section_heading))
story.append(Paragraph(
    "<b>Phase I</b> reactions introduce or unmask a polar functional group. "
    "The main enzyme system is the <b>cytochrome P450 (CYP450)</b> superfamily located in "
    "the hepatic smooth endoplasmic reticulum.",
    body))
phase1_data = [
    [Paragraph("<b>Reaction</b>", callout_style), Paragraph("<b>Description</b>", callout_style)],
    ["Oxidation", "Most common. CYP450-mediated. Adds oxygen or removes hydrogen."],
    ["Reduction", "Adds hydrogen / removes oxygen. Less common."],
    ["Hydrolysis", "Cleaves ester or amide bonds. Plasma esterases, intestinal enzymes."],
]
p1t = Table(phase1_data, colWidths=[4*cm, 13*cm])
p1t.setStyle(TableStyle([
    ("BACKGROUND",(0,0),(-1,0), TEAL),
    ("TEXTCOLOR",(0,0),(-1,0), WHITE),
    ("FONTNAME",(0,0),(-1,0),"Helvetica-Bold"),
    ("FONTSIZE",(0,0),(-1,-1),9.5),
    ("ROWBACKGROUNDS",(0,1),(-1,-1),[WHITE, LIGHT_GRAY]),
    ("GRID",(0,0),(-1,-1),0.4,MED_GRAY),
    ("TOPPADDING",(0,0),(-1,-1),6),
    ("BOTTOMPADDING",(0,0),(-1,-1),6),
    ("LEFTPADDING",(0,0),(-1,-1),8),
    ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
]))
story.append(p1t)
story.append(Spacer(1, 0.3*cm))

story.append(Paragraph("5.2  Phase II Reactions", section_heading))
story.append(Paragraph(
    "<b>Phase II</b> reactions are conjugation reactions. They attach an endogenous molecule to the "
    "drug or its Phase I metabolite. The resulting product is almost always pharmacologically inactive "
    "and more water-soluble, facilitating excretion.",
    body))
phase2_data = [
    ["Glucuronidation", "UDP-glucuronyltransferase. Most common Phase II reaction."],
    ["Sulfation", "Sulfotransferase. High affinity, low capacity."],
    ["Acetylation", "N-acetyltransferase. Genetic polymorphism: fast vs. slow acetylators."],
    ["Glutathione conjugation", "Protects against reactive metabolites (e.g., NAPQI from paracetamol)."],
    ["Methylation", "Catecholamines, histamine."],
]
p2t = Table(phase2_data, colWidths=[4.5*cm, 12.5*cm])
p2t.setStyle(TableStyle([
    ("BACKGROUND",(0,0),(0,-1), LIGHT_TEAL),
    ("FONTNAME",(0,0),(0,-1),"Helvetica-Bold"),
    ("FONTSIZE",(0,0),(-1,-1),9.5),
    ("GRID",(0,0),(-1,-1),0.4,MED_GRAY),
    ("TOPPADDING",(0,0),(-1,-1),6),
    ("BOTTOMPADDING",(0,0),(-1,-1),6),
    ("LEFTPADDING",(0,0),(-1,-1),6),
    ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
]))
story.append(p2t)
story.append(Spacer(1, 0.3*cm))

story.append(Paragraph("5.3  CYP450 Isoforms – Clinical Importance", section_heading))
cyp_data = [
    [Paragraph("<b>Isoform</b>", callout_style), Paragraph("<b>Major Substrates</b>", callout_style),
     Paragraph("<b>Inducers</b>", callout_style), Paragraph("<b>Inhibitors</b>", callout_style)],
    ["CYP3A4", "Cyclosporine, Midazolam, Simvastatin, Nifedipine, ~50% of all drugs",
     "Rifampicin, Carbamazepine, St John's Wort", "Ketoconazole, Erythromycin, Grapefruit juice"],
    ["CYP2D6", "Codeine (→Morphine), Beta-blockers, SSRIs, TCAs",
     "Rifampicin", "Fluoxetine, Paroxetine, Quinidine"],
    ["CYP2C9", "Warfarin (S-), Phenytoin, NSAIDs",
     "Rifampicin", "Amiodarone, Fluconazole, Metronidazole"],
    ["CYP2C19", "Omeprazole, Clopidogrel (prodrug→active), Diazepam",
     "Rifampicin", "Omeprazole, Fluoxetine"],
    ["CYP1A2", "Theophylline, Caffeine, Clozapine",
     "Smoking, Char-grilled meat", "Ciprofloxacin, Fluvoxamine"],
]
cyp = Table(cyp_data, colWidths=[2.5*cm, 5*cm, 5*cm, 4.5*cm])
cyp.setStyle(TableStyle([
    ("BACKGROUND",(0,0),(-1,0), NAVY),
    ("TEXTCOLOR",(0,0),(-1,0), WHITE),
    ("FONTNAME",(0,0),(-1,0),"Helvetica-Bold"),
    ("FONTSIZE",(0,0),(-1,-1),8.5),
    ("ROWBACKGROUNDS",(0,1),(-1,-1),[WHITE, LIGHT_GRAY]),
    ("GRID",(0,0),(-1,-1),0.4,MED_GRAY),
    ("TOPPADDING",(0,0),(-1,-1),5),
    ("BOTTOMPADDING",(0,0),(-1,-1),5),
    ("LEFTPADDING",(0,0),(-1,-1),5),
    ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
]))
story.append(cyp)
story.append(Spacer(1, 0.3*cm))
story.append(callout_box("Induction vs. Inhibition",
    ["Enzyme induction: increases metabolism → decreased drug levels → therapeutic failure. Takes days to weeks.",
     "Enzyme inhibition: decreases metabolism → increased drug levels → toxicity. Often immediate.",
     "Example: Rifampicin (inducer) + OCP → contraceptive failure",
     "Example: Ketoconazole (inhibitor) + Simvastatin → rhabdomyolysis risk",
     "CYP2D6 and CYP2C19 have genetic polymorphisms: poor vs. extensive metabolizers"],
    bg=RED_BOX, border=RED_BORDER, title_color=RED_BORDER))

story.append(Paragraph("5.4  Prodrugs", section_heading))
story.append(Paragraph(
    "A <b>prodrug</b> is an inactive precursor that requires metabolic conversion to produce the active form. "
    "They are used to improve bioavailability, stability, or targeting.",
    body))
story.append(Paragraph(
    "Examples: <b>Enalapril</b> → Enalaprilat (ACE inhibitor) | "
    "<b>Codeine</b> → Morphine (via CYP2D6) | "
    "<b>Clopidogrel</b> → Active thiol metabolite (via CYP2C19) | "
    "<b>Levodopa</b> → Dopamine (crosses BBB)",
    body))

# ═══════════════════════════════════════════════════════════════════
# SECTION 6 – ELIMINATION
# ═══════════════════════════════════════════════════════════════════
story.append(PageBreak())
story.append(section_divider("6  Elimination & Renal Excretion"))
story.append(Spacer(1, 0.3*cm))
story.append(Paragraph(
    "Elimination refers to the irreversible removal of drug from the body. "
    "The <b>kidneys</b> are the primary organ of excretion, though drugs may also be excreted "
    "in bile (→ feces), exhaled air, breast milk, sweat, and saliva.",
    body))

story.append(Paragraph("6.1  Renal Excretion – Three Processes", section_heading))
ren_data = [
    [Paragraph("<b>Process</b>", callout_style), Paragraph("<b>Mechanism</b>", callout_style),
     Paragraph("<b>Clinical Relevance</b>", callout_style)],
    ["Glomerular filtration", "Passive. Only free (unbound) drug filtered. Molecular weight <20,000 Da.",
     "Reduced in renal impairment → drug accumulation"],
    ["Tubular secretion", "Active transport (OAT, OCT transporters). Can excrete protein-bound drug.",
     "Drug-drug interactions (e.g., Probenecid blocks penicillin secretion)"],
    ["Tubular reabsorption", "Passive. Lipophilic/un-ionized drugs reabsorbed back into blood.",
     "Urinary pH manipulation: alkaline urine traps acidic drugs (aspirin OD treatment)"],
]
rent = Table(ren_data, colWidths=[4*cm, 7*cm, 6*cm])
rent.setStyle(TableStyle([
    ("BACKGROUND",(0,0),(-1,0), NAVY),
    ("TEXTCOLOR",(0,0),(-1,0), WHITE),
    ("FONTNAME",(0,0),(-1,0),"Helvetica-Bold"),
    ("FONTSIZE",(0,0),(-1,-1),9),
    ("ROWBACKGROUNDS",(0,1),(-1,-1),[WHITE, LIGHT_GRAY]),
    ("GRID",(0,0),(-1,-1),0.4,MED_GRAY),
    ("TOPPADDING",(0,0),(-1,-1),6),
    ("BOTTOMPADDING",(0,0),(-1,-1),6),
    ("LEFTPADDING",(0,0),(-1,-1),6),
    ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
]))
story.append(rent)
story.append(Spacer(1, 0.3*cm))

story.append(Paragraph("6.2  Biliary Excretion & Enterohepatic Circulation", section_heading))
story.append(Paragraph(
    "Drugs conjugated in the liver (especially glucuronides) are excreted into bile and enter the intestine. "
    "Gut bacteria can hydrolyze the conjugate, releasing the free drug, which is reabsorbed – "
    "this is <b>enterohepatic circulation</b>. It prolongs the drug's half-life. "
    "Examples: Oral contraceptives, Morphine, Chloramphenicol.",
    body))

story.append(Paragraph("6.3  Clearance (CL)", section_heading))
story.append(Paragraph(
    "<b>Clearance</b> is the volume of plasma cleared of drug per unit time (mL/min or L/h). "
    "It is the most important PK parameter for determining drug dosing.",
    body))
story.append(formula_box(
    "CL  =  Rate of elimination  /  Plasma drug concentration  =  Dose / AUC",
    "Drug Clearance"))
story.append(Spacer(1, 0.2*cm))
story.append(Paragraph(
    "Total clearance = Renal CL + Hepatic CL + other CL. "
    "CL is <b>independent of dose</b> for first-order drugs.",
    body))

# ═══════════════════════════════════════════════════════════════════
# SECTION 7 – KEY PK PARAMETERS
# ═══════════════════════════════════════════════════════════════════
story.append(PageBreak())
story.append(section_divider("7  Key Pharmacokinetic Parameters"))
story.append(Spacer(1, 0.3*cm))

param_data = [
    [Paragraph("<b>Parameter</b>", callout_style), Paragraph("<b>Symbol</b>", callout_style),
     Paragraph("<b>Formula</b>", callout_style), Paragraph("<b>Clinical Meaning</b>", callout_style)],
    ["Bioavailability", "F", "AUCoral / AUCIV", "Fraction reaching systemic circulation"],
    ["Volume of Distribution", "Vd", "Dose / C₀", "Drug distribution in body (L or L/kg)"],
    ["Clearance", "CL", "Dose / AUC", "Plasma volume cleared per unit time"],
    ["Half-life (elimination)", "t½", "0.693 × Vd / CL", "Time for plasma conc. to fall by 50%"],
    ["Elimination rate constant", "ke", "CL / Vd  = 0.693 / t½", "Fraction eliminated per unit time"],
    ["AUC", "AUC", "∫₀^∞ Cp·dt", "Total drug exposure; proportional to dose (linear PK)"],
    ["Cmax", "Cmax", "—", "Peak plasma concentration after dose"],
    ["Tmax", "Tmax", "—", "Time to reach peak plasma concentration"],
    ["Steady-state concentration", "Css", "Dosing rate / CL", "Avg. plasma conc. at steady state"],
    ["Loading dose", "LD", "Vd × Css / F", "Rapid attainment of therapeutic level"],
    ["Maintenance dose", "MD", "CL × Css × τ / F", "Dose to maintain steady state (τ = interval)"],
]
pd = Table(param_data, colWidths=[4*cm, 2*cm, 4.5*cm, 6.5*cm])
pd.setStyle(TableStyle([
    ("BACKGROUND",(0,0),(-1,0), NAVY),
    ("TEXTCOLOR",(0,0),(-1,0), WHITE),
    ("FONTNAME",(0,0),(-1,0),"Helvetica-Bold"),
    ("FONTSIZE",(0,0),(-1,-1),9),
    ("ROWBACKGROUNDS",(0,1),(-1,-1),[WHITE, LIGHT_GRAY]),
    ("GRID",(0,0),(-1,-1),0.4,MED_GRAY),
    ("TOPPADDING",(0,0),(-1,-1),5),
    ("BOTTOMPADDING",(0,0),(-1,-1),5),
    ("LEFTPADDING",(0,0),(-1,-1),5),
    ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
]))
story.append(pd)

# ═══════════════════════════════════════════════════════════════════
# SECTION 8 – HALF-LIFE & STEADY STATE
# ═══════════════════════════════════════════════════════════════════
story.append(PageBreak())
story.append(section_divider("8  Half-Life & Steady State"))
story.append(Spacer(1, 0.3*cm))

story.append(Paragraph("8.1  Elimination Half-Life (t½)", section_heading))
story.append(Paragraph(
    "The elimination half-life is the time required for the plasma concentration of a drug to "
    "decrease by 50%. For first-order kinetics, t½ is constant regardless of dose.",
    body))
story.append(formula_box("t½  =  0.693  ×  Vd / CL",
    "Half-life is determined by BOTH volume of distribution AND clearance"))
story.append(Spacer(1, 0.3*cm))

hl_data = [
    ["Half-lives elapsed", "1", "2", "3", "4", "5"],
    ["Drug remaining (%)", "50", "25", "12.5", "6.25", "3.13"],
    ["Drug eliminated (%)", "50", "75", "87.5", "93.75", "96.87"],
    ["Steady state achieved (%)", "50", "75", "87.5", "93.75", "96.87"],
]
hlt = Table(hl_data, colWidths=[5*cm]+[2.4*cm]*5)
hlt.setStyle(TableStyle([
    ("BACKGROUND",(0,0),(0,-1), NAVY),
    ("TEXTCOLOR",(0,0),(0,-1), WHITE),
    ("BACKGROUND",(0,0),(-1,0), TEAL),
    ("TEXTCOLOR",(0,0),(-1,0), WHITE),
    ("FONTNAME",(0,0),(-1,0),"Helvetica-Bold"),
    ("FONTNAME",(0,0),(0,-1),"Helvetica-Bold"),
    ("FONTSIZE",(0,0),(-1,-1),9.5),
    ("ALIGN",(1,0),(-1,-1),"CENTER"),
    ("ROWBACKGROUNDS",(0,1),(-1,-1),[WHITE, LIGHT_GRAY, WHITE]),
    ("GRID",(0,0),(-1,-1),0.4,MED_GRAY),
    ("TOPPADDING",(0,0),(-1,-1),6),
    ("BOTTOMPADDING",(0,0),(-1,-1),6),
    ("LEFTPADDING",(0,0),(-1,-1),6),
    ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
]))
story.append(hlt)
story.append(Spacer(1, 0.2*cm))
story.append(callout_box("Clinical Rules",
    ["Steady state (Css) is reached in approximately 4-5 half-lives",
     "50% of steady state is achieved after 1 half-life",
     "~90% of steady state is reached in 3.3 half-lives",
     "A drug is considered essentially eliminated after 5 half-lives",
     "Changing dose frequency does NOT change the rate of reaching steady state – only t½ determines this",
     "To reach steady state faster → give a loading dose"],
    bg=GREEN_BOX, border=GREEN_BORDER, title_color=GREEN_BORDER))

story.append(Paragraph("8.2  Order of Elimination Kinetics", section_heading))
story.append(Paragraph(
    "<b>First-order kinetics:</b> A constant <i>fraction</i> of drug is eliminated per unit time. "
    "Plasma concentration falls exponentially. t½ is constant. Applies to most drugs at therapeutic concentrations.",
    body))
story.append(Paragraph(
    "<b>Zero-order kinetics:</b> A constant <i>amount</i> of drug is eliminated per unit time "
    "(enzyme saturation). t½ is not constant – increases with dose. "
    "Classic examples: Ethanol (~10 mL/hr), Phenytoin at high doses, Aspirin in overdose.",
    body))
story.append(callout_box("Zero-order danger",
    ["When enzyme systems are saturated, a small increase in dose → disproportionately large rise in plasma levels",
     "Phenytoin toxicity: narrow therapeutic index + zero-order kinetics = high risk",
     "Ethanol: regardless of how much you drink, the liver can only metabolize ~1 unit/hour"],
    bg=RED_BOX, border=RED_BORDER, title_color=RED_BORDER))

# ═══════════════════════════════════════════════════════════════════
# SECTION 9 – DOSAGE REGIMENS
# ═══════════════════════════════════════════════════════════════════
story.append(PageBreak())
story.append(section_divider("9  Dosage Regimens"))
story.append(Spacer(1, 0.3*cm))

story.append(Paragraph("9.1  Steady State and Continuous Infusion", section_heading))
story.append(Paragraph(
    "When a drug is administered at a constant rate (IV infusion), plasma levels rise until the rate of "
    "input equals the rate of elimination – this is <b>steady state (Css)</b>. "
    "The time to reach Css depends only on t½, not on the infusion rate.",
    body))
story.append(formula_box("Css  =  Infusion rate / CL", "Steady-State Concentration (continuous IV infusion)"))
story.append(Spacer(1, 0.3*cm))

story.append(Paragraph("9.2  Loading Dose", section_heading))
story.append(Paragraph(
    "A <b>loading dose</b> rapidly achieves the desired therapeutic plasma concentration before the "
    "drug reaches steady state. It is particularly useful for drugs with long half-lives "
    "(e.g., Digoxin, Amiodarone, Phenytoin).",
    body))
story.append(formula_box(
    "Loading Dose  =  Vd  ×  Css  /  F",
    "Loading Dose Calculation"))
story.append(Spacer(1, 0.3*cm))

story.append(Paragraph("9.3  Maintenance Dose", section_heading))
story.append(Paragraph(
    "The <b>maintenance dose</b> is designed to maintain the steady-state concentration over time "
    "by replacing drug that has been eliminated during the dosing interval (τ).",
    body))
story.append(formula_box(
    "Maintenance Dose  =  CL  ×  Css  ×  τ  /  F",
    "Maintenance Dose Calculation (τ = dosing interval)"))
story.append(Spacer(1, 0.3*cm))

story.append(Paragraph("9.4  Therapeutic Drug Monitoring (TDM)", section_heading))
story.append(Paragraph(
    "TDM is used for drugs with a <b>narrow therapeutic index</b> where small concentration changes "
    "can cause toxicity or therapeutic failure.",
    body))
tdm_data = [
    [Paragraph("<b>Drug</b>", callout_style), Paragraph("<b>Therapeutic Range</b>", callout_style),
     Paragraph("<b>Toxicity Concern</b>", callout_style)],
    ["Digoxin", "0.5–2 ng/mL", "Arrhythmias, AV block"],
    ["Phenytoin", "10–20 mg/L", "Nystagmus, ataxia, encephalopathy"],
    ["Lithium", "0.6–1.2 mmol/L", "Tremor, nephrogenic DI, cardiac effects"],
    ["Gentamicin", "Peak 5–10 mg/L; Trough <2 mg/L", "Nephrotoxicity, ototoxicity"],
    ["Vancomycin", "Trough 10–20 mg/L (AUC/MIC guided)", "Nephrotoxicity, 'red man syndrome'"],
    ["Theophylline", "10–20 mg/L", "Arrhythmias, seizures"],
    ["Cyclosporine", "100–400 ng/mL (varies)", "Nephrotoxicity, hypertension"],
    ["Methotrexate", ">0.1 µmol/L at 48h is toxic", "Myelosuppression, mucositis"],
]
tdm = Table(tdm_data, colWidths=[4*cm, 5.5*cm, 7.5*cm])
tdm.setStyle(TableStyle([
    ("BACKGROUND",(0,0),(-1,0), NAVY),
    ("TEXTCOLOR",(0,0),(-1,0), WHITE),
    ("FONTNAME",(0,0),(-1,0),"Helvetica-Bold"),
    ("FONTSIZE",(0,0),(-1,-1),9),
    ("ROWBACKGROUNDS",(0,1),(-1,-1),[WHITE, LIGHT_GRAY]),
    ("GRID",(0,0),(-1,-1),0.4,MED_GRAY),
    ("TOPPADDING",(0,0),(-1,-1),5),
    ("BOTTOMPADDING",(0,0),(-1,-1),5),
    ("LEFTPADDING",(0,0),(-1,-1),5),
    ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
]))
story.append(tdm)

# ═══════════════════════════════════════════════════════════════════
# SECTION 10 – SPECIAL POPULATIONS
# ═══════════════════════════════════════════════════════════════════
story.append(PageBreak())
story.append(section_divider("10  Special Populations"))
story.append(Spacer(1, 0.3*cm))

sp_data = [
    [Paragraph("<b>Population</b>", callout_style), Paragraph("<b>PK Changes</b>", callout_style),
     Paragraph("<b>Clinical Action</b>", callout_style)],
    ["Renal Impairment",
     "↓ GFR → ↓ renal clearance → drug accumulation. Digoxin, aminoglycosides, lithium, metformin affected.",
     "Reduce dose or extend interval. Monitor drug levels. Avoid nephrotoxic drugs."],
    ["Hepatic Impairment",
     "↓ CYP450 activity → ↓ metabolism. ↓ albumin → ↑ free drug fraction. ↑ first-pass bypass via portosystemic shunts.",
     "Reduce doses of high-extraction drugs (morphine, midazolam). Avoid hepatotoxic drugs."],
    ["Elderly",
     "↓ GFR (normal aging). ↓ hepatic CYP activity. ↓ plasma albumin. ↑ body fat → ↑ Vd for lipophilic drugs. ↓ gastric acid.",
     "Start low, go slow. Adjust for renal function (Cockcroft-Gault). Beware polypharmacy."],
    ["Pediatrics",
     "Neonates: immature CYP450, immature renal function. Higher total body water → larger Vd for hydrophilic drugs.",
     "Weight-based dosing. Avoid chloramphenicol (grey baby syndrome), sulfonamides, aspirin (Reye syndrome)."],
    ["Pregnancy",
     "↑ GFR → ↑ renal clearance. ↑ plasma volume → dilution. ↑ CYP3A4 activity. ↓ albumin. Placental transfer concerns.",
     "Many drugs cross placenta – check pregnancy category. Folate supplementation important."],
    ["Obesity",
     "↑ Vd for lipophilic drugs. Variable changes in clearance. Loading dose may need adjustment.",
     "Use actual vs. ideal body weight carefully depending on drug lipophilicity."],
]
spt = Table(sp_data, colWidths=[3.5*cm, 7*cm, 6.5*cm])
spt.setStyle(TableStyle([
    ("BACKGROUND",(0,0),(-1,0), NAVY),
    ("TEXTCOLOR",(0,0),(-1,0), WHITE),
    ("FONTNAME",(0,0),(-1,0),"Helvetica-Bold"),
    ("FONTSIZE",(0,0),(-1,-1),8.5),
    ("ROWBACKGROUNDS",(0,1),(-1,-1),[WHITE, LIGHT_GRAY]),
    ("GRID",(0,0),(-1,-1),0.4,MED_GRAY),
    ("TOPPADDING",(0,0),(-1,-1),6),
    ("BOTTOMPADDING",(0,0),(-1,-1),6),
    ("LEFTPADDING",(0,0),(-1,-1),5),
    ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
]))
story.append(spt)

# ═══════════════════════════════════════════════════════════════════
# SECTION 11 – FORMULA REFERENCE
# ═══════════════════════════════════════════════════════════════════
story.append(PageBreak())
story.append(section_divider("11  Quick-Reference Formula Sheet"))
story.append(Spacer(1, 0.3*cm))

formulas = [
    ("Bioavailability", "F  =  AUC(oral) / AUC(IV)  ×  100%"),
    ("Volume of Distribution", "Vd  =  Dose / C₀"),
    ("Clearance", "CL  =  Dose / AUC  =  Rate of elimination / Cp"),
    ("Half-life", "t½  =  0.693 × Vd / CL"),
    ("Elimination rate constant", "ke  =  0.693 / t½  =  CL / Vd"),
    ("Steady-state (infusion)", "Css  =  Infusion rate / CL"),
    ("Steady-state (oral)", "Css(avg)  =  F × Dose / (CL × τ)"),
    ("Loading dose", "LD  =  Vd × Css / F"),
    ("Maintenance dose", "MD  =  CL × Css × τ / F"),
    ("Cockcroft-Gault (CrCl)", "CrCl  =  [(140−Age) × Weight(kg)] / [72 × Scr]  ×  0.85 (female)"),
]
for name, formula in formulas:
    row = Table([[
        Paragraph(name, ParagraphStyle("FName", fontSize=9.5, fontName="Helvetica-Bold",
                                       textColor=NAVY, alignment=TA_LEFT)),
        Paragraph(formula, ParagraphStyle("FFormula", fontSize=11, fontName="Helvetica-Bold",
                                          textColor=TEAL, alignment=TA_CENTER)),
    ]], colWidths=[5*cm, 12*cm])
    row.setStyle(TableStyle([
        ("BACKGROUND",(0,0),(-1,-1), LIGHT_TEAL),
        ("LINEBELOW",(0,0),(-1,-1),0.5,TEAL),
        ("TOPPADDING",(0,0),(-1,-1),8),
        ("BOTTOMPADDING",(0,0),(-1,-1),8),
        ("LEFTPADDING",(0,0),(-1,-1),8),
        ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
    ]))
    story.append(row)
    story.append(Spacer(1, 0.1*cm))

# ═══════════════════════════════════════════════════════════════════
# SECTION 12 – MCQ QUIZ
# ═══════════════════════════════════════════════════════════════════
story.append(PageBreak())
story.append(section_divider("12  MCQ-Style Revision Quiz"))
story.append(Spacer(1, 0.3*cm))

mcq_questions = [
    ("1", "A drug has a Vd of 42 L in a 70 kg patient. This suggests:",
     ["A. The drug is confined to plasma", "B. The drug distributes into total body water",
      "C. The drug is highly protein-bound", "D. The drug cannot cross cell membranes"], "B"),
    ("2", "Nitroglycerin is given sublingually rather than orally because:",
     ["A. It is not absorbed orally", "B. It has extensive first-pass hepatic metabolism",
      "C. It is too lipophilic for GI absorption", "D. It requires a sustained-release mechanism"], "B"),
    ("3", "A drug follows zero-order kinetics. Which of the following is TRUE?",
     ["A. Half-life is constant", "B. A constant fraction is eliminated per unit time",
      "C. A constant amount is eliminated per unit time", "D. AUC is proportional to dose"], "C"),
    ("4", "CYP2D6 poor metabolizers given codeine will:",
     ["A. Have enhanced morphine production and analgesia", "B. Have reduced morphine production and reduced analgesia",
      "C. Metabolize codeine faster", "D. Experience delayed onset but same peak effect"], "B"),
    ("5", "Which parameter is used to calculate the loading dose?",
     ["A. Clearance × half-life", "B. Vd × target Css / F",
      "C. AUC × bioavailability", "D. CL × dosing interval"], "B"),
    ("6", "A drug with a t½ of 8 hours is given by constant IV infusion. When is steady state reached?",
     ["A. 8 hours", "B. 16 hours", "C. 32–40 hours (4–5 × t½)", "D. 24 hours"], "C"),
    ("7", "Rifampicin is co-prescribed with warfarin. The most likely outcome is:",
     ["A. Warfarin levels increase → bleeding risk", "B. Warfarin levels decrease → thrombotic risk",
      "C. No interaction", "D. Warfarin becomes toxic"], "B"),
    ("8", "In alkaline urine, the clearance of aspirin (a weak acid) is:",
     ["A. Decreased – more reabsorbed", "B. Increased – more ionized and trapped in urine",
      "C. Unchanged", "D. Dependent on plasma protein binding"], "B"),
]

for q_num, question, options, answer in mcq_questions:
    q_block = [
        Paragraph(f"<b>Q{q_num}.</b> {question}", ParagraphStyle("Qtext",
            fontSize=10, fontName="Helvetica-Bold", textColor=NAVY,
            spaceAfter=4, spaceBefore=8, leading=14)),
    ]
    for opt in options:
        q_block.append(Paragraph(f"&nbsp;&nbsp;{opt}", ParagraphStyle("Opt",
            fontSize=9.5, fontName="Helvetica", leading=13, spaceAfter=2)))
    q_block.append(Paragraph(
        f"<font color='#27ae60'><b>Answer: {answer}</b></font>",
        ParagraphStyle("Ans", fontSize=9.5, fontName="Helvetica-Bold",
                       leading=13, spaceBefore=4, spaceAfter=4)))
    qbox = Table([[q_block]], colWidths=[17*cm])
    qbox.setStyle(TableStyle([
        ("BACKGROUND",(0,0),(-1,-1), LIGHT_GRAY),
        ("BOX",(0,0),(-1,-1),0.5,MED_GRAY),
        ("TOPPADDING",(0,0),(-1,-1),6),
        ("BOTTOMPADDING",(0,0),(-1,-1),6),
        ("LEFTPADDING",(0,0),(-1,-1),10),
        ("RIGHTPADDING",(0,0),(-1,-1),10),
    ]))
    story.append(qbox)
    story.append(Spacer(1, 0.2*cm))

# ── Final footer page ──
story.append(PageBreak())
footer_table = Table(
    [[Paragraph("Pharmacokinetics Study Guide", ParagraphStyle("FTTitle",
        fontSize=16, fontName="Helvetica-Bold", textColor=WHITE, alignment=TA_CENTER, spaceAfter=8)),
      Paragraph("Sources: Lippincott Illustrated Reviews: Pharmacology · Goodman &amp; Gilman's "
                "The Pharmacological Basis of Therapeutics · Katzung's Basic &amp; Clinical Pharmacology · "
                "Harrison's Principles of Internal Medicine",
                ParagraphStyle("FTSrc", fontSize=9, fontName="Helvetica", textColor=colors.HexColor("#c8e0f0"),
                               alignment=TA_CENTER, leading=13)),
    ]],
    colWidths=[17*cm],
)
footer_table.setStyle(TableStyle([
    ("BACKGROUND",(0,0),(-1,-1), NAVY),
    ("TOPPADDING",(0,0),(-1,-1),40),
    ("BOTTOMPADDING",(0,0),(-1,-1),40),
    ("LEFTPADDING",(0,0),(-1,-1),20),
    ("RIGHTPADDING",(0,0),(-1,-1),20),
]))
story.append(footer_table)

doc.build(story)
print(f"PDF created: {OUTPUT_PATH}")

cd /home/daytona/workspace/pharmacokinetics-guide && python generate_pk_guide.py

ls -lh /home/daytona/workspace/pharmacokinetics-guide/Pharmacokinetics_Study_Gui…