Buhera Script Examples

This document provides practical examples of Buhera scripts for various mass spectrometry applications.

Table of Contents

Biomarker Discovery

Diabetes Progression Biomarkers 

// diabetes_biomarkers.bh
// Comprehensive diabetes biomarker discovery with pathway focus

import lavoisier.mzekezeke
import lavoisier.hatata
import lavoisier.zengeza
import lavoisier.diggiden

objective DiabetesBiomarkerDiscovery:
    target: "identify metabolites predictive of diabetes progression from prediabetes to T2DM"
    success_criteria: "sensitivity >= 0.85 AND specificity >= 0.85 AND auc >= 0.9"
    evidence_priorities: "pathway_membership,clinical_correlation,ms2_fragmentation,mass_match"
    biological_constraints: "glycolysis_upregulated,insulin_signaling_disrupted,lipid_metabolism_altered"
    statistical_requirements: "sample_size >= 50, effect_size >= 0.5, power >= 0.8, fdr <= 0.05"

validate StudyDesign:
    check_sample_size
    if sample_size < 50:
        abort("Insufficient sample size for robust biomarker discovery")
    
    if case_control_ratio < 0.5 OR case_control_ratio > 2.0:
        warn("Unbalanced study design may introduce bias")

validate ClinicalData:
    if missing_hba1c_data:
        abort("HbA1c data required for diabetes progression assessment")
    
    if missing_fasting_glucose:
        warn("Fasting glucose data recommended for comprehensive assessment")

validate InstrumentCapability:
    check_instrument_capability
    if mass_accuracy > 3_ppm:
        warn("Mass accuracy may be insufficient for confident biomarker identification")
    
    if chromatographic_resolution < 10000:
        warn("Low chromatographic resolution may affect biomarker separation")

phase ClinicalDataIntegration:
    clinical_data = load_clinical_metadata(
        file_path: "diabetes_clinical_data.csv",
        required_fields: ["hba1c", "fasting_glucose", "progression_status"],
        time_points: ["baseline", "6_months", "12_months"]
    )
    
    // Validate clinical progression criteria
    progression_criteria = validate_progression_definitions(clinical_data)
    if NOT progression_criteria.valid:
        abort("Invalid progression criteria - check clinical definitions")

phase TargetedDataAcquisition:
    dataset = load_dataset(
        file_path: "diabetes_cohort_plasma.mzML",
        metadata: clinical_data,
        groups: ["control", "prediabetic", "t2dm"],
        focus: "diabetes_progression_markers"
    )
    
    // Focus on metabolites known to be diabetes-relevant
    targeted_ranges = [
        {"mz_min": 180.063, "mz_max": 180.067, "name": "glucose"},
        {"mz_min": 89.047, "mz_max": 89.051, "name": "lactate"},
        {"mz_min": 174.111, "mz_max": 174.115, "name": "arginine"}
    ]

phase PathwayFocusedPreprocessing:
    // Preserve signals relevant to diabetes pathways during cleanup
    clean_data = lavoisier.zengeza.noise_reduction(
        data: dataset,
        objective_context: "diabetes_biomarker_discovery",
        preserve_patterns: [
            "glucose_metabolism",
            "amino_acid_metabolism", 
            "lipid_metabolism",
            "insulin_signaling_metabolites"
        ],
        pathway_protection_level: "high"
    )

phase BiomarkerNetworkBuilding:
    // Build evidence network optimized for biomarker discovery
    biomarker_network = lavoisier.mzekezeke.build_evidence_network(
        data: clean_data,
        objective: "diabetes_biomarker_discovery",
        evidence_types: ["pathway_membership", "clinical_correlation", "ms2_fragmentation"],
        pathway_focus: [
            "glycolysis", 
            "gluconeogenesis",
            "lipid_metabolism",
            "amino_acid_metabolism",
            "insulin_signaling"
        ],
        evidence_weights: {
            "pathway_membership": 1.4,      // Biological relevance critical
            "clinical_correlation": 1.3,    // Clinical utility essential
            "ms2_fragmentation": 1.1,       // Structural confirmation
            "mass_match": 1.0,              // Basic identification
            "retention_time": 0.9           // Supporting evidence
        },
        clinical_integration: true
    )

phase StatisticalValidation:
    biomarker_candidates = lavoisier.hatata.validate_with_objective(
        evidence_network: biomarker_network,
        objective: "diabetes_biomarker_discovery",
        confidence_threshold: 0.85,
        clinical_utility_threshold: 0.8,
        statistical_tests: ["fold_change", "t_test", "auc_analysis"],
        multiple_testing_correction: "benjamini_hochberg"
    )
    
    // Require strong clinical performance
    validated_biomarkers = filter_clinical_performance(
        candidates: biomarker_candidates,
        min_auc: 0.9,
        min_sensitivity: 0.85,
        min_specificity: 0.85
    )

phase RobustnessTesting:
    robustness_results = lavoisier.diggiden.test_biomarker_robustness(
        biomarkers: validated_biomarkers,
        perturbation_types: [
            "batch_effects",
            "instrument_drift", 
            "population_variation",
            "storage_conditions"
        ],
        robustness_threshold: 0.8
    )
    
    robust_biomarkers = filter_robust_candidates(
        biomarkers: validated_biomarkers,
        robustness: robustness_results,
        min_stability: 0.8
    )

phase ClinicalTranslation:
    if robust_biomarkers.count >= 5:
        biomarker_panel = create_biomarker_panel(
            biomarkers: robust_biomarkers,
            panel_size: 10,
            optimization_target: "clinical_utility"
        )
        
        clinical_validation_plan = generate_clinical_validation_protocol(
            panel: biomarker_panel,
            study_design: "longitudinal_cohort",
            sample_size_calculation: true
        )
        
        export_clinical_targets(biomarker_panel, "diabetes_biomarker_targets.csv")
    else:
        suggest_study_improvements(robust_biomarkers, "insufficient_biomarkers")

Cancer Metabolomics Biomarkers 

// cancer_biomarkers.bh
// Cancer biomarker discovery with tumor metabolism focus

objective CancerBiomarkerDiscovery:
    target: "identify metabolites diagnostic of early-stage colorectal cancer"
    success_criteria: "sensitivity >= 0.90 AND specificity >= 0.85 AND auc >= 0.95"
    evidence_priorities: "pathway_membership,tumor_specificity,ms2_fragmentation"
    biological_constraints: "warburg_effect,glutamine_addiction,altered_lipogenesis"
    statistical_requirements: "sample_size >= 100, matched_controls >= 80, power >= 0.9"

validate TumorBiology:
    check_pathway_consistency
    if NOT warburg_effect_expected:
        warn("Warburg effect should be considered in cancer biomarker discovery")
    
    if glutamine_metabolism NOT prioritized:
        warn("Glutamine metabolism is critical in cancer - consider prioritizing")

phase CancerSpecificAnalysis:
    cancer_network = lavoisier.mzekezeke.build_evidence_network(
        objective: "cancer_biomarker_discovery",
        pathway_focus: [
            "glycolysis",
            "glutaminolysis", 
            "fatty_acid_synthesis",
            "nucleotide_synthesis",
            "one_carbon_metabolism"
        ],
        tumor_specificity_weighting: 1.5
    )

Drug Metabolism Studies

Hepatic Metabolism Characterization 

// hepatic_metabolism.bh
// Comprehensive characterization of drug metabolism pathways

import lavoisier.mzekezeke
import lavoisier.hatata
import lavoisier.zengeza

objective HepaticMetabolismCharacterization:
    target: "characterize complete metabolic profile of compound_XY123 in human hepatocytes"
    success_criteria: "metabolite_coverage >= 0.85 AND pathway_coherence >= 0.80"
    evidence_priorities: "ms2_fragmentation,enzymatic_specificity,retention_time,mass_match"
    biological_constraints: "cyp3a4_primary,cyp2d6_secondary,ugt_conjugation,gst_detox"
    statistical_requirements: "biological_replicates >= 6, technical_replicates >= 3"

validate MetabolismConditions:
    if temperature != 37_celsius:
        abort("Non-physiological temperature will affect metabolic rates")
    
    if co2_concentration != 5_percent:
        warn("Non-standard CO2 may affect cellular metabolism")
    
    if incubation_time < 2_hours:
        warn("Short incubation may miss slower metabolic processes")

validate EnzymeActivity:
    check_enzyme_activity
    if cyp3a4_activity < 0.7:
        warn("Low CYP3A4 activity - primary metabolism may be impaired")
    
    if ugt_activity < 0.5:
        warn("Low UGT activity - Phase II conjugation may be reduced")

phase TimeSeriesMetabolism:
    timepoints = ["0h", "0.5h", "1h", "2h", "4h", "8h", "24h"]
    
    metabolic_progression = {}
    
    for timepoint in timepoints:
        timepoint_data = load_dataset(
            file_path: f"hepatocytes_{timepoint}.mzML",
            metadata: f"enzyme_activity_{timepoint}.csv"
        )
        
        timepoint_network = lavoisier.mzekezeke.build_evidence_network(
            data: timepoint_data,
            objective: "hepatic_metabolism_characterization",
            evidence_types: ["ms2_fragmentation", "enzymatic_specificity"],
            pathway_focus: [
                "cyp1a2", "cyp2c9", "cyp2c19", "cyp2d6", "cyp3a4",
                "ugt1a1", "ugt1a4", "ugt2b7",
                "gst_alpha", "gst_mu", "gst_pi"
            ],
            evidence_weights: {
                "ms2_fragmentation": 1.4,    // Structure critical
                "enzymatic_specificity": 1.3, // Pathway assignment
                "retention_time": 1.1,       // Chromatographic confirmation
                "mass_match": 1.0            // Basic identification
            },
            temporal_context: timepoint
        )
        
        metabolic_progression[timepoint] = timepoint_network

phase MetabolicPathwayReconstruction:
    pathway_map = reconstruct_metabolic_pathways(
        time_series: metabolic_progression,
        parent_compound: "compound_XY123",
        expected_transformations: [
            "hydroxylation",
            "oxidation", 
            "glucuronidation",
            "sulfation",
            "glutathione_conjugation"
        ]
    )
    
    if pathway_map.coverage >= 0.85:
        validated_pathways = lavoisier.hatata.validate_pathway_coherence(
            pathways: pathway_map,
            enzyme_kinetics: enzyme_activity_data,
            literature_validation: true
        )
    else:
        suggest_extended_timepoints(pathway_map)

phase MetaboliteStructuralElucidation:
    for metabolite in validated_pathways.novel_metabolites:
        structural_evidence = lavoisier.collect_structural_evidence(
            metabolite: metabolite,
            evidence_types: ["ms2_fragmentation", "accurate_mass", "isotope_pattern"],
            confidence_threshold: 0.9
        )
        
        if structural_evidence.confidence >= 0.9:
            proposed_structure = predict_metabolite_structure(
                parent: "compound_XY123",
                transformation: metabolite.transformation_type,
                spectral_evidence: structural_evidence
            )
            
            validate_structure_with_literature(proposed_structure)

Drug-Drug Interaction Study 

// drug_interaction.bh
// Metabolic drug-drug interaction characterization

objective DrugDrugInteractionStudy:
    target: "characterize metabolic interactions between compound_A and compound_B"
    success_criteria: "interaction_detection >= 0.9 AND mechanism_clarity >= 0.8"
    evidence_priorities: "enzymatic_inhibition,metabolite_shift,kinetic_changes"
    biological_constraints: "competitive_inhibition,allosteric_effects,induction"
    statistical_requirements: "dose_levels >= 5, replicates >= 4, controls >= 3"

validate InteractionDesign:
    if dose_range_ratio < 10:
        warn("Narrow dose range may miss dose-dependent interactions")
    
    if missing_individual_controls:
        abort("Individual compound controls required for interaction assessment")

phase InteractionAnalysis:
    interaction_matrix = analyze_metabolic_interactions(
        compound_a_data: load_dataset("compound_a_alone.mzML"),
        compound_b_data: load_dataset("compound_b_alone.mzML"),
        combination_data: load_dataset("compounds_combined.mzML"),
        interaction_types: ["competitive", "noncompetitive", "uncompetitive"]
    )

Environmental Analysis

Pesticide Residue Detection 

// pesticide_analysis.bh
// Comprehensive pesticide residue analysis in food samples

import lavoisier.mzekezeke
import lavoisier.hatata
import lavoisier.zengeza

objective PesticideResidueDetection:
    target: "detect and quantify pesticide residues in agricultural products"
    success_criteria: "detection_limit <= 0.01_mg_kg AND false_positive_rate <= 0.02"
    evidence_priorities: "accurate_mass,isotope_pattern,retention_time,fragmentation"
    biological_constraints: "environmental_degradation,matrix_effects,extraction_efficiency"
    statistical_requirements: "matrix_blanks >= 10, spiked_samples >= 20, method_blanks >= 5"

validate ExtractionMethod:
    if extraction_method == "liquid_liquid" AND hydrophilic_pesticides_expected:
        warn("Liquid-liquid extraction may miss hydrophilic pesticides")
    
    if extraction_method == "solid_phase" AND volatile_compounds_expected:
        warn("SPE may lose volatile pesticides - consider headspace analysis")

validate MatrixEffects:
    check_matrix_compatibility
    if matrix_suppression > 0.2:
        warn("Significant matrix suppression detected - consider matrix-matched calibration")
    
    if matrix_enhancement > 0.15:
        warn("Matrix enhancement may lead to overestimation")

phase PesticideTargetedAnalysis:
    pesticide_database = load_pesticide_database(
        regions: ["north_america", "europe"],
        compound_classes: ["organophosphates", "neonicotinoids", "triazines"],
        regulatory_limits: true
    )
    
    targeted_analysis = lavoisier.mzekezeke.build_evidence_network(
        objective: "pesticide_residue_detection",
        target_list: pesticide_database,
        evidence_types: ["accurate_mass", "isotope_pattern", "retention_time"],
        evidence_weights: {
            "accurate_mass": 1.3,        // Critical for identification
            "isotope_pattern": 1.2,      // Confirms molecular formula
            "retention_time": 1.1,       // Chromatographic confirmation
            "fragmentation": 1.0         // Structural support
        },
        detection_threshold: 0.01_mg_kg
    )

phase SuspectScreening:
    suspect_compounds = lavoisier.screen_suspect_compounds(
        data: dataset,
        suspect_list: "transformation_products.csv",
        mass_tolerance: 5_ppm,
        rt_tolerance: 0.2_min
    )
    
    if suspect_compounds.detected > 0:
        further_investigation = lavoisier.investigate_unknowns(
            suspects: suspect_compounds,
            evidence_requirements: ["ms2_spectrum", "accurate_mass"]
        )

phase RegulatoryCompliance:
    regulatory_assessment = assess_regulatory_compliance(
        detected_pesticides: targeted_analysis.confirmed,
        regulatory_limits: pesticide_database.limits,
        region: "north_america"
    )
    
    if regulatory_assessment.violations > 0:
        generate_violation_report(regulatory_assessment)
        recommend_followup_analysis(regulatory_assessment.violations)

Water Contamination Analysis 

// water_contamination.bh
// Environmental water contamination assessment

objective WaterContaminationAssessment:
    target: "identify and quantify emerging contaminants in drinking water"
    success_criteria: "detection_coverage >= 0.9 AND quantification_accuracy >= 0.8"
    evidence_priorities: "environmental_relevance,persistence,toxicity_potential"
    biological_constraints: "bioaccumulation,endocrine_disruption,antibiotic_resistance"
    statistical_requirements: "sampling_sites >= 15, seasonal_samples >= 4"

validate SamplingStrategy:
    if sampling_sites < 15:
        warn("Limited sampling sites may not represent water system complexity")
    
    if NOT seasonal_variation_considered:
        warn("Seasonal contamination patterns may be missed")

phase EmergingContaminantScreening:
    contaminant_screening = lavoisier.mzekezeke.build_evidence_network(
        objective: "water_contamination_assessment",
        compound_classes: [
            "pharmaceuticals",
            "personal_care_products", 
            "industrial_chemicals",
            "pesticide_metabolites",
            "pfas_compounds"
        ],
        environmental_priority_weighting: 1.4
    )

Food Safety Testing

Mycotoxin Detection 

// mycotoxin_analysis.bh
// Comprehensive mycotoxin analysis in food products

import lavoisier.mzekezeke
import lavoisier.hatata

objective MycotoxinDetection:
    target: "detect regulated mycotoxins in cereal products"
    success_criteria: "detection_limit <= regulatory_threshold AND specificity >= 0.98"
    evidence_priorities: "regulatory_importance,toxicity_level,accurate_mass,ms2_confirmation"
    biological_constraints: "fungal_origin,environmental_conditions,storage_effects"
    statistical_requirements: "matrix_blanks >= 8, certified_standards >= 5"

validate FoodMatrix:
    if moisture_content > 0.14:
        warn("High moisture content may promote additional mycotoxin formation")
    
    if storage_temperature > 25_celsius:
        warn("Elevated storage temperature may affect mycotoxin stability")

validate AnalyticalStandards:
    if missing_certified_standards:
        abort("Certified reference standards required for regulatory compliance")
    
    if standard_purity < 0.98:
        warn("Low standard purity may affect quantification accuracy")

phase RegulatedMycotoxinAnalysis:
    regulated_mycotoxins = [
        {"name": "aflatoxin_b1", "limit": 2_ug_kg, "priority": "high"},
        {"name": "aflatoxin_b2", "limit": 2_ug_kg, "priority": "high"},
        {"name": "ochratoxin_a", "limit": 5_ug_kg, "priority": "medium"},
        {"name": "deoxynivalenol", "limit": 1750_ug_kg, "priority": "medium"},
        {"name": "zearalenone", "limit": 100_ug_kg, "priority": "medium"}
    ]
    
    mycotoxin_network = lavoisier.mzekezeke.build_evidence_network(
        objective: "mycotoxin_detection",
        target_compounds: regulated_mycotoxins,
        evidence_types: ["accurate_mass", "ms2_fragmentation", "retention_time"],
        evidence_weights: {
            "regulatory_importance": 1.5,  // Regulatory priority
            "accurate_mass": 1.3,          // Identification confidence
            "ms2_fragmentation": 1.2,      // Structural confirmation
            "retention_time": 1.1          // Chromatographic confirmation
        },
        quantification_mode: true
    )

phase RegulatoryReporting:
    regulatory_results = lavoisier.hatata.validate_regulatory_compliance(
        detected_mycotoxins: mycotoxin_network.quantified,
        regulatory_limits: regulated_mycotoxins,
        region: "european_union",
        confidence_threshold: 0.99
    )
    
    if regulatory_results.violations > 0:
        generate_regulatory_report(
            violations: regulatory_results.violations,
            format: "official_certificate"
        )

Allergen Detection 

// allergen_analysis.bh
// Food allergen protein detection and quantification

objective AllergenDetection:
    target: "detect and quantify allergenic proteins in processed foods"
    success_criteria: "sensitivity >= 1_mg_kg AND cross_reactivity <= 0.05"
    evidence_priorities: "protein_specificity,allergenic_potency,processing_stability"
    biological_constraints: "protein_denaturation,processing_effects,matrix_interference"
    statistical_requirements: "allergen_free_controls >= 10, spiked_levels >= 5"

validate ProcessingEffects:
    if high_temperature_processing:
        warn("High temperature may denature proteins - consider peptide analysis")
    
    if fermentation_process:
        warn("Fermentation may degrade proteins - validate detection limits")

phase AllergenProteinAnalysis:
    allergen_targets = [
        {"protein": "ara_h1", "source": "peanut", "potency": "high"},
        {"protein": "gly_m4", "source": "soy", "potency": "medium"},
        {"protein": "tri_a14", "source": "wheat", "potency": "high"}
    ]
    
    protein_detection = lavoisier.mzekezeke.build_evidence_network(
        objective: "allergen_detection",
        protein_targets: allergen_targets,
        peptide_analysis: true,
        processing_tolerance: "heat_stable_peptides"
    )

Clinical Metabolomics

Personalized Medicine 

// personalized_medicine.bh
// Individual metabolic phenotyping for personalized treatment

objective PersonalizedMetabolicPhenotyping:
    target: "characterize individual metabolic response to medication"
    success_criteria: "phenotype_accuracy >= 0.9 AND therapeutic_prediction >= 0.85"
    evidence_priorities: "genetic_correlation,enzymatic_activity,metabolic_capacity"
    biological_constraints: "cyp_genotype,transporter_activity,comorbidities"
    statistical_requirements: "individual_baseline >= 3, post_treatment >= 5"

validate GeneticData:
    if missing_cyp_genotype:
        warn("CYP genotype data recommended for metabolism prediction")
    
    if missing_transporter_variants:
        warn("Transporter variants may affect drug disposition")

phase IndividualPhenotyping:
    individual_profile = lavoisier.mzekezeke.build_evidence_network(
        objective: "personalized_metabolic_phenotyping",
        individual_data: patient_metabolomics,
        genetic_integration: cyp_genotype_data,
        phenotype_prediction: true
    )

Precision Dosing 

// precision_dosing.bh
// Metabolomics-guided precision dosing

objective PrecisionDosingOptimization:
    target: "optimize drug dosing based on individual metabolic capacity"
    success_criteria: "dosing_accuracy >= 0.9 AND adverse_event_reduction >= 0.7"
    evidence_priorities: "metabolic_capacity,clearance_prediction,safety_margins"
    biological_constraints: "age_effects,organ_function,drug_interactions"
    statistical_requirements: "pk_timepoints >= 8, individual_replicates >= 3"

phase MetabolicCapacityAssessment:
    metabolic_assessment = assess_individual_metabolic_capacity(
        baseline_metabolomics: patient_baseline,
        probe_drug_response: probe_metabolism_data,
        genetic_factors: pharmacogenomic_profile
    )
    
    optimized_dosing = calculate_precision_dose(
        metabolic_capacity: metabolic_assessment,
        target_exposure: therapeutic_window,
        safety_factors: individual_risk_profile
    )

Quality Control

Method Validation 

// method_validation.bh
// Comprehensive analytical method validation

objective AnalyticalMethodValidation:
    target: "validate LC-MS method for therapeutic drug monitoring"
    success_criteria: "precision <= 0.15 AND accuracy >= 0.85 AND recovery >= 0.80"
    evidence_priorities: "analytical_performance,robustness,matrix_independence"
    biological_constraints: "physiological_range,interference_potential,stability"
    statistical_requirements: "validation_runs >= 6, qc_levels >= 3, replicates >= 6"

validate ValidationDesign:
    if qc_levels < 3:
        abort("Minimum 3 QC levels required for method validation")
    
    if validation_runs < 6:
        abort("Minimum 6 validation runs required for statistical validity")

phase PrecisionValidation:
    precision_data = collect_precision_data(
        intra_day_runs: 6,
        inter_day_runs: 6,
        qc_levels: ["low", "medium", "high"],
        replicates_per_level: 6
    )
    
    precision_results = calculate_precision_metrics(
        data: precision_data,
        acceptance_criteria: {"cv_percent": 15}
    )

phase AccuracyValidation:
    accuracy_data = collect_accuracy_data(
        certified_standards: true,
        spiked_samples: true,
        recovery_levels: [50, 100, 150]  // % of target concentration
    )
    
    accuracy_results = calculate_accuracy_metrics(
        data: accuracy_data,
        acceptance_criteria: {"bias_percent": 15}
    )

phase RobustnessValidation:
    robustness_factors = [
        "mobile_phase_ph",
        "column_temperature", 
        "injection_volume",
        "flow_rate"
    ]
    
    robustness_results = lavoisier.diggiden.test_method_robustness(
        factors: robustness_factors,
        variation_levels: ["low", "nominal", "high"],
        critical_pairs: [["ph", "temperature"]]
    )

Instrument Performance Monitoring 

// instrument_qc.bh
// Continuous instrument performance monitoring

objective InstrumentPerformanceMonitoring:
    target: "monitor LC-MS instrument performance for early detection of issues"
    success_criteria: "performance_drift <= 0.1 AND uptime >= 0.95"
    evidence_priorities: "mass_accuracy,sensitivity,chromatographic_performance"
    biological_constraints: "temperature_stability,contamination_buildup,wear_patterns"
    statistical_requirements: "qc_frequency >= daily, trending_points >= 20"

validate QCFrequency:
    if qc_injections_per_day < 4:
        warn("Low QC frequency may miss performance drift")
    
    if system_suitability_interval > 50_samples:
        warn("Infrequent system suitability may miss instrument issues")

phase ContinuousMonitoring:
    qc_monitoring = setup_continuous_qc_monitoring(
        qc_standards: ["mass_accuracy", "retention_time", "peak_area"],
        control_limits: {"2_sigma": "warning", "3_sigma": "fail"},
        trending_analysis: true
    )
    
    performance_alerts = monitor_instrument_performance(
        qc_data: qc_monitoring,
        alert_thresholds: performance_limits,
        predictive_maintenance: true
    )

Method Development

Chromatographic Optimization 

// method_development.bh
// Systematic chromatographic method development

objective ChromatographicOptimization:
    target: "develop optimal LC separation for multi-class pharmaceutical analysis"
    success_criteria: "resolution >= 1.5 AND peak_capacity >= 100 AND runtime <= 20_min"
    evidence_priorities: "separation_quality,analysis_time,robustness"
    biological_constraints: "compound_stability,pH_sensitivity,temperature_effects"
    statistical_requirements: "optimization_experiments >= 20, replicates >= 3"

validate CompoundProperties:
    if compound_pka_range > 3:
        warn("Wide pKa range may require gradient pH optimization")
    
    if hydrophobicity_range > 4_logp_units:
        warn("Large hydrophobicity range may require extended gradient")

phase StatisticalOptimization:
    optimization_design = create_doe_experiment(
        factors: [
            {"name": "gradient_slope", "range": [5, 30]},  // %B/min
            {"name": "column_temperature", "range": [30, 50]},  // °C
            {"name": "mobile_phase_ph", "range": [2.0, 8.0]}
        ],
        design_type: "central_composite",
        response_variables: ["resolution", "analysis_time", "peak_tailing"]
    )
    
    optimization_results = execute_optimization_experiments(
        design: optimization_design,
        evaluation_criteria: method_objectives
    )
    
    optimal_conditions = find_optimal_conditions(
        results: optimization_results,
        desirability_function: multi_response_optimization
    )

phase MethodValidationPrep:
    method_conditions = optimal_conditions.best_compromise
    
    preliminary_validation = validate_preliminary_method(
        conditions: method_conditions,
        test_compounds: target_analytes,
        validation_parameters: ["precision", "linearity", "range"]
    )
    
    if preliminary_validation.success:
        recommend_full_validation(method_conditions)
    else:
        suggest_method_refinement(preliminary_validation.issues)

This comprehensive collection of examples demonstrates how Buhera scripts encode scientific reasoning for various analytical applications, ensuring that every analysis step is directed toward achieving specific, measurable objectives.

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