Pharmacology

Pharmacokinetics (ADME)

Absorption

class Pharmacokinetics:
    """Pharmacokinetic modeling"""
    
    def calculate_clearance(self, dose, auc):
        """
        Clearance = Dose / AUC
        Units: L/hr or mL/min
        """
        return dose / auc
    
    def calculate_volume_of_distribution(self, dose, concentration):
        """
        Vd = Dose / C₀
        Volume in which drug would need to be distributed
        """
        return dose / concentration
    
    def half_life_calculation(self, Vd, Cl):
        """
        t½ = 0.693 × Vd / Cl
        """
        return 0.693 * Vd / Cl
    
    def loading_dose(self, target_conc, Vd, bioavailability=1):
        """
        Loading dose = (Target × Vd) / F
        """
        return (target_conc * Vd) / bioavailability
    
    def maintenance_dose(self, target_conc, Cl, dosing_interval, F=1):
        """
        Maintenance dose = Target × Cl × τ / F
        """
        return target_conc * Cl * dosing_interval / F

Distribution

Protein Binding

# Free drug hypothesis
# Only free drug is pharmacologically active

def free_drug_concentration(total_conc, fu):
    """
    fu = fraction unbound
    C_free = C_total × fu
    """
    return total_conc * fu

def adjust_total_for_protein_binding(total_conc, fu_new, fu_old):
    """
    Adjust dose when protein binding changes
    """
    free_old = total_conc * fu_old
    total_new = free_old / fu_new
    return total_new

Pharmacodynamics

Drug-Receptor Interactions

class ReceptorTheory:
    """Drug-receptor interaction models"""
    
    # Hill equation (Emax model)
    def emax_model(self, E_max, C, EC50):
        """
        E = E_max × C / (EC50 + C)
        """
        return E_max * C / (EC50 + C)
    
    def hill_equation(self, E_max, C, EC50, n):
        """
        E = E_max × Cⁿ / (EC50ⁿ + Cⁿ)
        n = Hill coefficient (cooperativity)
        """
        return E_max * (C ** n) / (EC50 ** n + C ** n)
    
    def competitive_antagonism(self, agonist_conc, antagonist_conc, pA2):
        """
        Schild analysis for competitive antagonism
        pA2 = -log(Kb)
        Dose ratio = 1 + [Ant]/Kb
        """
        dose_ratio = 1 + antagonist_conc / (10 ** (-pA2))
        return dose_ratio

Dose-Response Relationships

Drug Metabolism

Phase I Reactions

Phase II Reactions

class DrugMetabolism:
    """Drug metabolism prediction"""
    
    # Common CYP450 substrates, inducers, inhibitors
    CYP450_PROPERTIES = {
        "CYP3A4": {
            "substrates": ["atorvastatin", "clarithromycin", "simvastatin"],
            "inducers": ["rifampin", "carbamazepine", "phenytoin"],
            "inhibitors": ["ketoconazole", "erythromycin", "grapefruit"]
        },
        "CYP2D6": {
            "substrates": ["metoprolol", "tramadol", "codeine"],
            "inhibitors": ["fluoxetine", "paroxetine", "quinidine"]
        },
        "CYP2C9": {
            "substrates": ["warfarin", "phenytoin"],
            "inhibitors": ["fluconazole", "amiodarone"]
        },
        "CYP2C19": {
            "substrates": ["omeprazole", "clopidogrel"],
            "inhibitors": ["fluoxetine", "omeprazole"]
        }
    }
    
    # Drug-drug interactions
    def predict_interaction(self, drug1, drug2, mechanism):
        """
        Mechanisms: competitive inhibition, induction,
        substrate competition, displacement
        """
        interactions = {
            "competitive_inhibition": "Increase substrate levels",
            "induction": "Decrease substrate levels",
            "displacement": "Increase free drug (if highly bound)"
        }
        return interactions.get(mechanism, "Unknown")

Drug Interactions

Interaction Types

# Common clinically significant interactions
CLINICALLY_SIGNIFICANT = {
    "warfarin + NSAIDs": "Increased bleeding risk",
    "warfarin + metronidazole": "Increased INR",
    "digoxin + amiodarone": "Increased digoxin levels",
    "clarithromycin + simvastatin": "Rhabdomyolysis risk",
    "methotrexate + NSAIDs": "Increased toxicity",
    "ACEI + potassium": "Hyperkalemia",
    "SSRI + tramadol": "Serotonin syndrome",
    "QT prolonging drugs + other QT drugs": "Torsades"
}

Toxicology

Dose-Response Curves

Response
    ▲
100%│───────────────────────────────────────►
    │          ┌────── Toxicity
    │        ╱ 
    │      ╱ 
    │    ╱
    │  ╱
    │╱────────────── Therapeutic effect
    └──────────────────────────────────────►
              Dose

Triage and Management

Clinical Pharmacology

Therapeutic Drug Monitoring

Drug Dosing in Special Populations

Common Errors to Avoid

1. Ignoring drug half-life — Affects dosing interval and time to steady state
2. Forgetting first-pass metabolism — Oral vs. IV dosing can differ dramatically
3. Not checking protein binding — Free drug determines effect in displacement
4. Missing drug-drug interactions — Review all medications at each visit
5. Inappropriate TDM timing — Trough vs. peak matters
6. Not adjusting for organ dysfunction — Renal/hepatic impairment
7. Ignoring pharmacogenomics — CYP2D6, CYP2C19 polymorphism affects many drugs
8. Confusing potency with efficacy — EC₅₀ vs. E_max