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  • Adverse Drug Reactions and Drug Safety Profiles: A Comprehensive Review of Classification, Mechanisms, Clinical Evaluation, and Pharmacovigilance Strategies

  • Pravara Institute of Medical Sciences (Deemed University), College of Pharmaceutical Sciences, Loni, Maharashtra 413736, India.

Abstract

Background: Adverse drug reactions (ADRs) are a leading cause of preventable patient harm worldwide, accounting for 3–5% of hospital admissions and significant morbidity and mortality. A mechanistic understanding of ADR types, combined with rigorous safety evaluation and pharmacovigilance, is essential for minimising drug-related harm in clinical practice.Objectives: This review aims to systematically characterise ADR classification (Types A–F), evaluate drug safety assessment across the drug development lifecycle, and appraise evidence-based pharmacovigilance and ADR management strategies relevant to current clinical and regulatory practice.Methods: A comprehensive narrative review was conducted using PubMed/MEDLINE, Embase, and Cochrane Library databases (January 2000–December 2024), supplemented by regulatory guidance documents from the WHO, FDA, EMA, and CDSCO. Studies were selected for mechanistic relevance, clinical significance, and publication quality.Conclusion: Effective ADR management requires integration of mechanism-based pharmacology, structured clinical trial safety assessment, adaptive pharmacovigilance systems, and emerging tools including pharmacogenomics and AI-assisted clinical decision support. The evidence reviewed supports a multidisciplinary, patient-centred approach to drug safety in 2025–2026 practice.

Keywords

adverse drug reactions; pharmacovigilance; drug safety profile; Naranjo algorithm; clinical trials; pharmacogenomics; post-marketing surveillance; Type A–F classification; drug-induced harm; patient safety

Introduction

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Adverse drug reactions (ADRs) represent a major public health challenge, affecting millions of patients globally each year. The World Health Organization (WHO) defines an ADR as a response to a drug that is noxious, unintended, and occurs at doses normally used for prophylaxis, diagnosis, or treatment. [1,3] ADRs account for approximately 3–5% of all unplanned hospital admissions and are implicated in adverse outcomes in 10–20% of hospitalised patients. [1,4] The economic burden is equally substantial; ADR-attributable hospitalisation costs in the United States alone are estimated at over USD 30 billion annually. [2,10]

The aetiology of ADRs is complex and multifactorial, arising from pharmacological mechanisms inherent to the drug, patient-related biological variables (age, sex, genetic polymorphisms, comorbidity, concurrent medications), and healthcare system factors (prescribing practices, monitoring quality). [3,9] Genetic variability in drug-metabolising enzymes—particularly the cytochrome P450 (CYP450) superfamily—is a key determinant of interindividual susceptibility to both concentration-dependent and idiosyncratic reactions. [11,18]

Despite significant advances in drug safety science, post-marketing experience continues to reveal ADR profiles that were incompletely characterised during pre-approval trials, reflecting inherent limitations in trial sample size, duration, and population diversity. [5,20] Contemporary pharmacovigilance infrastructure—encompassing spontaneous reporting systems, electronic health record analytics, disproportionality analysis, and pharmacogenomic risk stratification—represents the primary tool for bridging this gap. [14,16]

This review provides a structured, evidence-based analysis of ADR classification, mechanistic basis, drug safety evaluation methodology, and pharmacovigilance strategies, with the objective of supporting safe and rational pharmacotherapy in clinical practice.

2. Classification of Adverse Drug Reactions

The Rawlins–Thompson classification, extended by Aronson and Ferner, remains the most clinically applicable framework for ADR categorisation. [6,7] It stratifies ADRs into six mechanistically distinct types (A–F), enabling systematic clinical recognition, causality assessment, and pharmacovigilance signal interpretation. Table 1 provides a structured overview.

 

Table 1. The Rawlins–Thompson–Aronson Classification of Adverse Drug Reactions (Types A–F)

Type

Nomenclature

Key Features

Clinical Examples

Type A

Augmented (dose-dependent)

Predictable; pharmacological; dose-related; most common (~80%)

Morphine — constipation; ACE inhibitor — dry cough; insulin — hypoglycaemia

Type B

Bizarre (idiosyncratic)

Unpredictable; immune-mediated or genetic; not dose-related

Penicillin — anaphylaxis; sulfonamide — Stevens–Johnson syndrome

Type C

Chronic (continuous use)

Cumulative dose-dependent; may be irreversible

Corticosteroids — osteoporosis; antipsychotics — tardive dyskinesia

Type D

Delayed onset

Latency of months to years; carcinogenic or teratogenic

Thalidomide — phocomelia; chemotherapy — secondary malignancy

Type E

End-of-use (withdrawal)

On abrupt discontinuation of chronically used drugs

Opioid withdrawal; benzodiazepine seizures; adrenal insufficiency

Type F

Failure of therapy

Therapeutic ineffectiveness; dose- or interaction-related

Oral contraceptive failure with rifampicin; clopidogrel in CYP2C19 PM

ADR = adverse drug reaction; SJS = Stevens–Johnson syndrome; PM = poor metaboliser; HPA = hypothalamic-pituitary-adrenal

 

2.1 Type A (Augmented) Reactions

Type A reactions are dose-dependent, pharmacologically predictable extensions of the drug's primary mechanism of action and account for approximately 80% of all ADRs in clinical practice. [6,8] They are generally manageable through dose adjustment, therapeutic drug monitoring, or formulation modification. Common examples include hypoglycaemia from insulin, anticoagulant-induced haemorrhage, opioid-induced respiratory depression, and ACE inhibitor-associated dry cough, which occurs in up to 15% of treated patients through bradykinin accumulation. [3,4]

2.2 Type B (Bizarre/Idiosyncratic) Reactions

Type B reactions are pharmacologically unpredictable, not dose-related in conventional terms, and frequently involve immune activation or aberrant metabolic pathways. [3,7] They account for fewer than 10–15% of ADRs but are disproportionately associated with fatal outcomes and drug withdrawals. IgE-mediated anaphylaxis to penicillin, Stevens–Johnson syndrome and toxic epidermal necrolysis (SJS/TEN) with sulfonamides and anticonvulsants, and clozapine-induced agranulocytosis are representative examples. [11,54] Specific HLA alleles have been identified as major susceptibility determinants: HLA-B*57:01 predicts abacavir hypersensitivity with >95% sensitivity, and HLA-B*15:02 is strongly associated with carbamazepine-induced SJS/TEN in Han Chinese populations. [11,37]

2.3 Type C (Chronic) Reactions

Type C reactions depend on cumulative drug exposure and manifest only after prolonged or repeated administration, reflecting sustained pathological adaptation. [6,9] Corticosteroid-induced osteoporosis, bisphosphonate-related osteonecrosis of the jaw, tardive dyskinesia from antipsychotics, and anticholinergic-associated cognitive impairment in elderly patients are classical examples. The potential irreversibility of many Type C reactions underscores the importance of regular clinical monitoring and exposure minimisation. [31,32]

2.4 Type D (Delayed) Reactions

Type D reactions are time-dependent adverse effects emerging after a latency of months to years, including carcinogenic, teratogenic, and genotoxic consequences. [6,9] Thalidomide-induced phocomelia, the paradigmatic Type D event, led directly to mandatory reproductive toxicity testing requirements now embedded in ICH guidelines. Contemporary examples include secondary malignancies following alkylating agent chemotherapy, delayed peripheral neuropathy from oxaliplatin, and leucopenia emerging up to six weeks after lomustine administration. [23,39,40]

2.5 Type E (End-of-Use) Reactions

Type E reactions arise on abrupt discontinuation or rapid tapering of drugs that have induced neuroadaptation or physiological dependence during chronic use. [6] Opioid withdrawal syndrome, rebound hypertension on abrupt clonidine cessation, benzodiazepine withdrawal seizures, adrenal insufficiency following corticosteroid tapering, and SSRI discontinuation syndrome all represent this category. These reactions are almost universally preventable through individualised, gradual tapering protocols. [8,35]

2.6 Type F (Failure of Therapy) Reactions

Type F adverse reactions represent unexpected therapeutic failure—a category of ADR in which the drug fails to deliver the anticipated clinical benefit. [7] This is generally dose-related and arises from pharmacokinetic drug–drug interactions reducing drug exposure, acquired resistance, or inadequate dosing. Examples include reduced oral contraceptive efficacy during rifampicin co-administration (CYP3A4 induction), clopidogrel treatment failure in CYP2C19 poor metabolisers, and antimicrobial underperformance in patients undergoing renal replacement therapy. [18,19]

3. Adverse Drug Reactions Across Major Pharmacological Drug Classes

Understanding class-specific ADR profiles is fundamental to safe prescribing and clinical monitoring. Table 2 presents a mechanistic summary of ADRs across 15 pharmacological drug classes of high clinical relevance, incorporating representative agents, principal adverse effects, and mechanistic basis.

 

Table 2. Adverse Drug Reaction Profiles Across Selected Major Pharmacological Drug Classes

Drug Class (Representative Agents)

Principal ADRs

Mechanistic Basis

Ref.

NSAIDs (e.g., ibuprofen, naproxen)

GI ulceration, renal impairment, platelet inhibition, hypertension

COX-1 inhibition reduces prostaglandin-mediated GI mucosal protection

[16,17]

Opioids (morphine, fentanyl, codeine)

Respiratory depression, constipation, sedation, dependence

Mu-opioid receptor activation suppresses medullary respiratory centre

[16,18]

Beta-lactam antibiotics (amoxicillin, ceftriaxone)

Anaphylaxis, rash, antibiotic-associated diarrhoea, nephritis

IgE-mediated sensitisation to beta-lactam ring; gut microbiome disruption

[19,20]

Fluoroquinolones (ciprofloxacin, levofloxacin)

QTc prolongation, tendinopathy, peripheral neuropathy, phototoxicity

hERG channel blockade; mitochondrial toxicity in tendons and nerves

[21,22]

Aminoglycosides (gentamicin, amikacin)

Nephrotoxicity, ototoxicity (cochlear and vestibular)

Drug accumulates in renal proximal tubular cells and cochlear hair cells

[23,24]

Antitubercular drugs (isoniazid, rifampicin)

Drug-induced liver injury (DILI), peripheral neuropathy, hyperuricaemia

CYP2E1-mediated reactive metabolite generation; B6 antagonism (INH)

[25,26]

Statins (atorvastatin, rosuvastatin)

Myopathy, rhabdomyolysis, new-onset diabetes mellitus, hepatotoxicity

CoQ10 depletion; impaired mitochondrial function in skeletal muscle

[27,28]

Anticoagulants (warfarin, rivaroxaban, heparin)

Major haemorrhage, heparin-induced thrombocytopaenia (HIT)

Factor Xa/thrombin inhibition; immune platelet activation (HIT type II)

[29,30]

Corticosteroids (prednisolone, dexamethasone)

Hyperglycaemia, osteoporosis, HPA axis suppression, Cushing syndrome

Chronic glucocorticoid receptor activation alters metabolic homeostasis

[31,32]

2nd-generation antipsychotics (olanzapine, clozapine)

Metabolic syndrome, weight gain, agranulocytosis (clozapine)

D2/5-HT2A blockade; H1 antagonism; bone marrow toxicity (clozapine)

[33,34]

SSRIs/SNRIs (fluoxetine, sertraline, venlafaxine)

Sexual dysfunction, serotonin syndrome, hyponatraemia, GI upset

SERT inhibition; SIADH via hypothalamic serotonin stimulation

[35,36]

Anticonvulsants (phenytoin, valproate, carbamazepine)

SJS/TEN, gingival hyperplasia, teratogenicity, hepatotoxicity

HLA-B*15:02 mediated immune injury; CYP2C9 polymorphism (phenytoin)

[37,38]

Alkylating chemotherapy (cyclophosphamide, cisplatin)

Myelosuppression, haemorrhagic cystitis, nephrotoxicity, neuropathy

DNA alkylation; acrolein-mediated urothelial toxicity (cyclophosphamide)

[39,40]

Calcineurin inhibitors (ciclosporin, tacrolimus)

Nephrotoxicity, hypertension, neurotoxicity, opportunistic infections

Renal afferent arteriolar vasoconstriction; immune suppression

[41,42]

SGLT2 inhibitors (empagliflozin, dapagliflozin)

Euglycaemic DKA, genitourinary infections, volume depletion

Urinary glucose promotes fungal overgrowth; insulin suppression elevates ketogenesis

[43,44]

ADR = adverse drug reaction; DKA = diabetic ketoacidosis; GI = gastrointestinal; HIT = heparin-induced thrombocytopaenia; HPA = hypothalamic-pituitary-adrenal; INH = isoniazid; QTc = corrected QT interval; SJS/TEN = Stevens–Johnson syndrome/toxic epidermal necrolysis.

 

4. Drug Safety Evaluation: From Preclinical Studies to Post-Marketing Surveillance

The safety evaluation of a new drug constitutes a systematic, multistage continuum from early preclinical characterisation through post-marketing surveillance. [5,23] Each stage progressively broadens the characterisation of benefit–risk profile across larger and more diverse populations.

4.1 Preclinical Safety Assessment

Preclinical safety studies are conducted under Good Laboratory Practice (GLP) standards mandated by ICH guideline M3(R2) before first-in-human (FIH) administration. [23] The preclinical programme encompasses acute and repeat-dose toxicity studies (14-day to 12-month protocols in rodent and non-rodent species), safety pharmacology assessments targeting cardiovascular, CNS, and respiratory systems, genotoxicity batteries (Ames test, chromosome aberration assay, in vivo micronucleus test), and reproductive and developmental toxicity investigations.

From these data, the No-Observed-Adverse-Effect Level (NOAEL) is derived and used—with allometric scaling and appropriate safety margins—to calculate the maximum recommended starting dose (MRSD) for Phase I human trials. [23,25] A critical limitation of preclinical models is their imperfect translation to human ADR prediction; species-specific differences in metabolism, receptor pharmacology, and immunological mechanisms account for important translational gaps that contribute to the attrition of drug candidates in later development phases.

4.2 Investigational New Drug (IND) Application

Initiation of clinical trials in the United States requires submission of an Investigational New Drug (IND) application to the FDA, comprising preclinical pharmacology and toxicology data, chemistry, manufacturing, and control (CMC) information, detailed clinical trial protocols, and investigator qualifications. [24] Equivalent mechanisms exist under the EMA Clinical Trial Authorisation (CTA) framework and India's CDSCO Form CT-04. Institutional Review Board (IRB)/Ethics Committee approval and formal informed consent procedures are mandatory prerequisites across all jurisdictions.

4.3 Clinical Trial Phases

Clinical trials progress sequentially from Phase 0 (microdosing) through Phase IV (post-marketing surveillance), each with distinct safety objectives and participant characteristics. [24,26] Table 3 provides a structured overview of all phases with respect to population, dosing strategy, primary objectives, and key safety outcomes.

 

Table 3. Sequential Phases of Clinical Drug Development: Design Parameters and Safety Objectives

Phase

Population

Dosing

Primary Objective

Key Safety Outcome

Phase 0

10–15 healthy

Subtherapeutic microdose

ADME characterisation, PK preliminary data

Drug behaviour; decision to proceed

Phase I

20–100 healthy volunteers

Escalating dose

Safety, tolerability, PK/PD, MTD determination

Safe dose range, adverse event profile

Phase II

100–300 patients

Optimised therapeutic range

Efficacy proof-of-concept, dose optimisation, safety

Confirmation of clinical benefit

Phase III

Hundreds–thousands patients

Established therapeutic dose

Comparative efficacy, safety vs standard therapy, rare ADR detection

Regulatory submission (NDA/MAA)

Phase IV

General population

Approved regimen

Long-term safety, rare ADRs, special populations, pharmacoeconomics

Label updates, risk minimisation, withdrawal if warranted

ADME = absorption, distribution, metabolism, excretion; MTD = maximum tolerated dose; NDA = new drug application; MAA = marketing authorisation application; PK = pharmacokinetics; PD = pharmacodynamics.

 

A critical statistical limitation of Phase III trials is their inability to detect rare ADRs occurring below a frequency of 1 in 1,000. To detect an event occurring at 1:10,000 frequency with 95% confidence would require enrolment of approximately 30,000 patients. [26,29] This epidemiological reality renders Phase IV post-marketing surveillance indispensable for defining the complete ADR spectrum of any approved medicine.

4.4 New Drug Application (NDA) and Regulatory Approval

Successful Phase III completion triggers NDA submission to the FDA or Marketing Authorisation Application (MAA) to the EMA. Regulatory review synthesises all preclinical and clinical safety, efficacy, and CMC data to assess whether the benefit–risk balance justifies approval for the proposed indication and patient population. [24] Where uncertainties persist, conditional approval may be granted with mandatory post-marketing study commitments, enhanced spontaneous reporting requirements, or implementation of a Risk Evaluation and Mitigation Strategy (REMS). [17]

5. Pharmacovigilance and ADR Management Strategies

Pharmacovigilance—defined by the WHO as the science and activities relating to the detection, assessment, understanding, and prevention of adverse effects or any other medicine-related problem—is central to ongoing drug safety monitoring. [5,16] The WHO's International Drug Monitoring Programme, coordinated through the Uppsala Monitoring Centre (UMC), maintains VigiBase, the world's largest ADR repository with over 35 million reports from 150 member nations. [14] Table 4 summarises current pharmacovigilance methods and their clinical significance.

 

Table 4. Pharmacovigilance Strategies: Methods, Implementation, and Clinical Significance

Strategy

Description

Clinical Significance

Spontaneous Reporting Systems (SRS)

Healthcare professionals and patients voluntarily report suspected ADRs to national centres (FAERS, PvPI, Yellow Card, EudraVigilance); mandatory reporting for serious events

Signal generation; limited by underreporting (estimated 6–10% of ADRs reported)

Prescription Event Monitoring (PEM)

Prospective cohort surveillance tracking all clinical events in patients receiving newly marketed drugs, via prescription database linkage

Detection of rare early-onset ADRs; free of underreporting bias compared to SRS

Electronic Health Record (EHR) Mining

NLP-based automated extraction of ADR signals from structured and unstructured clinical records across hospital networks

High sensitivity; enables detection of long-latency ADRs; requires confounding control

Disproportionality Analysis (PRR/ROR)

Statistical comparison of observed-to-expected reporting ratios in large databases (VigiBase: >35 million reports, 2024)

Quantitative signal detection; guides regulatory signal review

Pharmacogenomic (PGx) Screening

Pre-treatment genotyping for high-risk alleles: HLA-B*57:01 (abacavir), HLA-B*15:02 (carbamazepine), CYP2C19 (clopidogrel), TPMT (thiopurines)

Precision risk stratification; CPIC guidelines endorsed by FDA and EMA

Therapeutic Drug Monitoring (TDM)

Serial measurement of serum drug levels (aminoglycosides, vancomycin, lithium, ciclosporin, phenytoin) with Bayesian dose optimisation

Maintains drugs within therapeutic window; prevents concentration-dependent toxicity

Clinical Decision Support Systems (CDSS)

AI-integrated EHR tools providing real-time alerts for drug–drug interactions, contraindications, dose adjustments in renal/hepatic impairment

Reduces prescribing errors; alert fatigue (override rates 49–96%) remains a challenge

Risk Evaluation and Mitigation Strategies (REMS)

Mandatory FDA programmes for high-risk drugs: restricted distribution, patient enrolment, prescriber certification, medication guides

Clozapine (ANC monitoring); isotretinoin (iPLEDGE); thalidomide (STEPS programme)

Deprescribing and Polypharmacy Management

Systematic rationalisation of medications using Beers Criteria (AGS 2023) and STOPP/START v3, particularly in elderly patients (≥65 years)

Reduces polypharmacy-associated ADR burden; lowers 30-day readmission rates

ANC = absolute neutrophil count; CPIC = Clinical Pharmacogenomics Implementation Consortium; EMA = European Medicines Agency; FAERS = FDA Adverse Event Reporting System; NLP = natural language processing; PvPI = Pharmacovigilance Programme of India; REMS = Risk Evaluation and Mitigation Strategy; STOPP/START = Screening Tool of Older Persons' Prescriptions/Screening Tool to Alert to Right Treatment.

 

5.1 Causality Assessment

Establishing causality between drug exposure and a reported adverse event is foundational to pharmacovigilance practice. The Naranjo Algorithm, developed in 1981, employs a 10-item scoring system incorporating temporal relationships, de-challenge and re-challenge outcomes, literature support, and confounders to classify causality as definite (≥9), probable (5–8), possible (1–4), or doubtful (≤0). [13] The WHO–UMC system uses six ordinal categories (certain, probable, possible, unlikely, conditional/unclassified, unassessable) and is preferred for national pharmacovigilance centre reporting. [46] The Hartwig and Siegel Severity Scale stratifies ADR severity from Level 1 (mild) to Level 7 (fatal), enabling risk prioritisation within surveillance datasets. [15]

5.2 Pharmacogenomics and Precision Safety

The integration of pharmacogenomic (PGx) data into clinical practice constitutes one of the most significant advances in ADR prevention. Pre-therapeutic genotyping for clinically actionable alleles—including CYP2D6 (codeine, tamoxifen), CYP2C19 (clopidogrel, proton pump inhibitors), TPMT/NUDT15 (thiopurines), HLA-B*57:01 (abacavir), and HLA-B*15:02 (carbamazepine)—is now supported by CPIC implementation guidelines. [11,54] These guidelines provide actionable prescribing recommendations based on genotype, fundamentally altering the individualised approach to ADR prevention.

5.3 Digital Health and AI-Driven Signal Detection

Large-scale electronic health records, federated data networks, and NLP algorithms have substantially expanded capacity for real-world ADR signal detection. Machine learning models trained on EHR data demonstrate superior sensitivity for drug–event associations compared with traditional spontaneous reporting, particularly for ADRs with long latency periods. [48] Clinical Decision Support Systems (CDSS) integrated into hospital information systems provide real-time alerts for high-risk drug–drug interactions and contraindications; however, alert fatigue—evidenced by clinician override rates of 49–96% in published studies—remains a significant implementation challenge. [48]

5.4 Special Populations and Polypharmacy

Paediatric, geriatric, pregnant, and renally or hepatically impaired patients are systematically underrepresented in clinical trials and represent pharmacologically vulnerable subgroups with heightened ADR risk. [53] In older adults, physiological changes including reduced hepatic and renal clearance, decreased albumin binding, and heightened CNS sensitivity increase Type A ADR vulnerability. The Beers Criteria (AGS 2023) and STOPP/START v3 provide validated frameworks for identifying potentially inappropriate prescriptions in this population. [51,52]

Polypharmacy—defined as concurrent use of five or more medications—increases drug interaction potential exponentially. A meta-analysis by Leelakanok et al. demonstrated a significant, dose-dependent association between number of concurrent medications and all-cause mortality in older adults. [51] Structured deprescribing interventions using validated tools represent a primary evidence-based strategy for reducing polypharmacy-associated ADR burden.

6. Evidence-Based Strategies for ADR Prevention and Management

A comprehensive ADR management programme requires multiple complementary strategies across the prescribing, dispensing, administration, and monitoring continuum. The following measures are endorsed by WHO, FDA, EMA, NICE, and major clinical pharmacology societies. [5,49,50]

6.1 ADR Reporting and Pharmacovigilance

Systematic reporting of all suspected serious, unexpected, or clinically significant ADRs to national pharmacovigilance programmes is both a professional obligation and a key mechanism for population-level safety signal generation. [16,17] Healthcare professionals should report regardless of certainty of causation; even uncertain reports contribute to signal detection through disproportionality analysis.

6.2 Therapeutic Drug Monitoring (TDM)

For drugs with narrow therapeutic indices—including aminoglycosides, vancomycin, lithium, digoxin, phenytoin, ciclosporin, and tacrolimus—serial measurement of serum drug concentrations with guided dose adjustment is essential for toxicity prevention. [47] Population pharmacokinetic modelling and Bayesian dose optimisation are increasingly used to individualise TDM-based dosing in critical care and transplant settings.

6.3 Medication Reconciliation

Structured medication reconciliation at all care transitions prevents transcription errors, inadvertent omissions, and drug duplications that constitute major ADR risk factors. [50] The WHO High 5s initiative established standardised operating procedures for medication reconciliation that have demonstrated significant reductions in medication error-related harm across participating countries.

6.4 Patient Education and Shared Decision-Making

Effective patient counselling on expected and unexpected adverse effects, symptom recognition, and appropriate response thresholds is a fundamental component of safe pharmacotherapy. [49] NICE CG76 (Medicines Adherence) advocates patient-centred communication strategies that balance information provision with patient engagement and self-management empowerment.

6.5 Deprescribing and Polypharmacy Rationalisation

Structured deprescribing—the supervised, intentional withdrawal of medications for which harms outweigh expected benefits—is a proactive ADR prevention strategy of particular relevance in older adults and patients with multimorbidity. [51,62] Application of the Beers Criteria, STOPP/START criteria, and the STOPPFrail list provides validated guidance for identifying and rationalising potentially inappropriate prescriptions in complex patients.

6.6 Risk Evaluation and Mitigation Strategies (REMS)

For medicines with serious risks not manageable through standard labelling, the FDA mandates REMS programmes comprising medication guides, communication plans, restricted distribution systems, and patient enrolment requirements. [17] Active examples include clozapine REMS (absolute neutrophil count monitoring), iPLEDGE for isotretinoin (teratogenicity prevention), and the STEPS programme for thalidomide (prevention of embryo exposure).

CONCLUSION

Adverse drug reactions remain an intrinsic and substantial hazard of modern pharmacotherapy, yet the evidence clearly demonstrates that the majority are preventable through rigorous mechanistic understanding, structured clinical surveillance, and robust pharmacovigilance. [1,3,4] The Rawlins–Thompson–Aronson classification provides a durable, practically applicable framework for organising diverse ADR phenomena according to mechanistic logic, enabling clinicians and regulators to move beyond pattern recognition toward predictive, individualised risk management. [6,7] Accurate ADR classification informs immediate clinical response, regulatory policy, and prescribing guidelines. The drug safety evaluation lifecycle—from GLP-compliant preclinical toxicology through sequential clinical trial phases to open-ended post-marketing surveillance—represents the pharmaceutical industry's best effort to characterise drug safety across the full range of patient populations. [5,23,26] Inherent limitations of pre-approval trials make perpetual post-marketing vigilance indispensable, underpinned by spontaneous reporting, real-world data analytics, and adaptive pharmacovigilance infrastructure. Emerging advances in pharmacogenomics, artificial intelligence, and digital health represent the leading edge of ADR science in 2025–2026. Realising their full potential for preventing avoidable harm and optimising therapeutic outcomes requires sustained investment in surveillance systems, interprofessional education, regulatory innovation, and patient engagement. [11,48,63] In synthesis, safe pharmacotherapy is not a static endpoint but a dynamic, continuous, and collaborative endeavour—requiring partnership between patients, clinicians, pharmacists, researchers, industry, and regulators, united by the shared imperative to maximise benefit and minimise preventable harm. [5,65]

Declarations

Conflict of Interest

The authors declare no conflict of interest relevant to the subject matter of this review.

Funding

This review received no specific funding from any agency in the public, commercial, or not-for-profit sectors.

Authors' Contributions

Rutuja Anarthe1: Conceptualisation, literature review, original draft preparation, critical revision of intellectual content.

Apeksha Kadu: Literature search, data synthesis, manuscript editing.

Rushikesh S. Gaikwad and Shubham N. Kanawade: Methodology, manuscript review, reference compilation.

All authors have read, critically reviewed, and approved the final manuscript.

Ethical Approval

Not applicable. This is a narrative review article; no human subjects research or animal experimentation was conducted.

 

 

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  32. Manson JE, Chlebowski RT, Stefanick ML, Aragaki AK, Rossouw JE, Prentice RL, et al. Menopausal hormone therapy and health outcomes during the intervention and extended poststopping phases of the Women's Health Initiative randomized trials. JAMA. 2013;310(13):1353–68.
  33. Leucht S, Cipriani A, Spineli L, Mavridis D, Örey D, Richter F, et al. Comparative efficacy and tolerability of 15 antipsychotic drugs in schizophrenia: a multiple-treatments meta-analysis. Lancet. 2013;382(9896):951–62.
  34. Tiihonen J, Lönnqvist J, Wahlbeck K, Klaukka T, Niskanen L, Tanskanen A, et al. 11-year follow-up of mortality in patients with schizophrenia: a population-based cohort study. Lancet. 2009;374(9690):620–7.
  35. Cipriani A, Furukawa TA, Salanti G, Chaimani A, Atkinson LZ, Ogawa Y, et al. Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder. Lancet. 2018;391(10128):1357–66.
  36. Gartlehner G, Hansen RA, Morgan LC, Thaler K, Lux L, Van Noord M, et al. Comparative benefits and harms of second-generation antidepressants for treating major depressive disorder. Ann Intern Med. 2011;155(11):772–85.
  37. Arif H, Buchsbaum R, Weintraub D, Koyfman S, Salas-Humara C, Bazil CW, et al. Comparison and predictors of rash associated with 15 antiepileptic drugs. Neurology. 2007;68(20):1701–9.
  38. Tomson T, Battino D, Bonizzoni E, Craig J, Lindhout D, Sabers A, et al. Dose-dependent risk of malformations with antiepileptic drugs. Neurology. 2011;77(20):1826–34.
  39. Hartmann JT, Lipp HP. Camptothecin and podophyllotoxin derivatives: inhibitors of topoisomerase I and II – mechanisms of action, pharmacokinetics and toxicity profile. Drug Saf. 2006;29(3):209–30.
  40. Chabner BA, Roberts TG Jr. Timeline: chemotherapy and the war on cancer. Nat Rev Cancer. 2005;5(1):65–72.
  41. Bamgbola O. Review of vancomycin-induced renal complications. Ther Adv Endocrinol Metab. 2016;7(3):136–47.
  42. Staatz CE, Tett SE. Clinical pharmacokinetics and pharmacodynamics of tacrolimus in solid organ transplantation. Clin Pharmacokinet. 2004;43(10):623–53.
  43. Zinman B, Wanner C, Lachin JM, Fitchett D, Bluhmki E, Hantel S, et al. Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med. 2015;373(22):2117–28.
  44. Neal B, Perkovic V, Mahaffey KW, de Zeeuw D, Fulcher G, Erondu N, et al. Canagliflozin and cardiovascular and renal events in type 2 diabetes. N Engl J Med. 2017;377(7):644–57.
  45. Mehta U, Dheda M, Steel G, Blockman M, Pillay S. Strengthening pharmacovigilance in South Africa. S Afr Med J. 2014;104(2):104–5.
  46. WHO-UMC. The use of the WHO–UMC system for standardised case causality assessment. Uppsala: Uppsala Monitoring Centre; 2005.
  47. Ghiculescu RA. Therapeutic drug monitoring: which drugs, why, when and how to do it. Aust Prescr. 2008;31(2):42–4.
  48. Bates DW, Teich JM, Lee J, Seger D, Kuperman GJ, Ma'Luf N, et al. The impact of computerized physician order entry on medication error prevention. J Am Med Inform Assoc. 1999;6(4):313–21.
  49. NICE. Medicines adherence: involving patients in decisions about prescribed medicines. Clinical Guideline CG76. London: NICE; 2009.
  50. WHO. Patient safety solutions: medication reconciliation. High 5s Initiative. Geneva: WHO; 2007.
  51. Leelakanok N, Holcombe AL, Lund BC, Gu X, Schweizer ML. Association between polypharmacy and death: a systematic review and meta-analysis. J Am Pharm Assoc. 2017;57(6):729–38.
  52. Gnjidic D, Hilmer SN. Pharmacological basis of polypharmacy and ADRs. Clin Pharmacol Ther. 2015;97(5):468–70.
  53. US Food and Drug Administration. Guidance for industry: Drug interaction studies. FDA; 2020.
  54. Phillips EJ, Mallal SA. Pharmacogenetics of drug hypersensitivity. Pharmacogenomics. 2004;5(7):877–89.
  55. Ritter JM, Flower R, Henderson G, Loke YK, MacEwan D, Rang HP. Rang and Dale's Pharmacology. 9th ed. Edinburgh: Elsevier; 2020.
  56. Moore N, Lecointre D, Noblet C, Mabille M. Frequency and cost of serious adverse drug reactions in a department of general medicine. Br J Clin Pharmacol. 1998;45(3):301–8.
  57. Varricchio C, Pierce M, Dolan CB, Johnson KA. A Cancer Source Book for Nurses. 8th ed. Burlington: Jones & Bartlett; 2003.
  58. Meyboom RH, Egberts AC, Edwards IR, Hekster YA, de Koning FH, Gribnau FW. Principles of signal detection in pharmacovigilance. Drug Saf. 1997;16(6):355–65.
  59. Bégaud B, Martin K, Haramburu F, Moore N. Rates of spontaneous reporting of adverse drug reactions in France. JAMA. 2002;288(13):1588.
  60. Hazell L, Shakir SA. Under-reporting of adverse drug reactions: a systematic review. Drug Saf. 2006;29(5):385–96.
  61. Avery AJ, Dex GM, Mulvaney C, Serumaga B, Spencer R, Lester HE, et al. Development of prescribing-safety indicators for GPs using the RAND appropriateness method. Br J Gen Pract. 2011;61(589):e526–36.
  62. Scott IA, Hilmer SN, Reeve E, Potter K, Le Couteur D, Rigby D, et al. Reducing inappropriate polypharmacy: the process of deprescribing. JAMA Intern Med. 2015;175(5):827–34.
  63. WHO-UMC. Pharmacovigilance: ensuring the safe use of medicines. Policy perspectives on medicines No. 009. Geneva: WHO; 2004.
  64. Kalisch LM, Caughey GE, Barratt JD, Gilbert AL, Roughead EE. Prevalence of preventable medication-related hospitalisations in Australia. Med J Aust. 2012;197(2):97–101.
  65. Aronson JK. Medication errors: definitions and classification. Br J Clin Pharmacol. 2009;67(6):599–604.

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  31. Fardet L, Petersen I, Nazareth I. Prevalence of long-term oral glucocorticoid prescriptions in the UK over the past 20 years. Rheumatology (Oxford). 2011;50(11):1982–90.
  32. Manson JE, Chlebowski RT, Stefanick ML, Aragaki AK, Rossouw JE, Prentice RL, et al. Menopausal hormone therapy and health outcomes during the intervention and extended poststopping phases of the Women's Health Initiative randomized trials. JAMA. 2013;310(13):1353–68.
  33. Leucht S, Cipriani A, Spineli L, Mavridis D, Örey D, Richter F, et al. Comparative efficacy and tolerability of 15 antipsychotic drugs in schizophrenia: a multiple-treatments meta-analysis. Lancet. 2013;382(9896):951–62.
  34. Tiihonen J, Lönnqvist J, Wahlbeck K, Klaukka T, Niskanen L, Tanskanen A, et al. 11-year follow-up of mortality in patients with schizophrenia: a population-based cohort study. Lancet. 2009;374(9690):620–7.
  35. Cipriani A, Furukawa TA, Salanti G, Chaimani A, Atkinson LZ, Ogawa Y, et al. Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder. Lancet. 2018;391(10128):1357–66.
  36. Gartlehner G, Hansen RA, Morgan LC, Thaler K, Lux L, Van Noord M, et al. Comparative benefits and harms of second-generation antidepressants for treating major depressive disorder. Ann Intern Med. 2011;155(11):772–85.
  37. Arif H, Buchsbaum R, Weintraub D, Koyfman S, Salas-Humara C, Bazil CW, et al. Comparison and predictors of rash associated with 15 antiepileptic drugs. Neurology. 2007;68(20):1701–9.
  38. Tomson T, Battino D, Bonizzoni E, Craig J, Lindhout D, Sabers A, et al. Dose-dependent risk of malformations with antiepileptic drugs. Neurology. 2011;77(20):1826–34.
  39. Hartmann JT, Lipp HP. Camptothecin and podophyllotoxin derivatives: inhibitors of topoisomerase I and II – mechanisms of action, pharmacokinetics and toxicity profile. Drug Saf. 2006;29(3):209–30.
  40. Chabner BA, Roberts TG Jr. Timeline: chemotherapy and the war on cancer. Nat Rev Cancer. 2005;5(1):65–72.
  41. Bamgbola O. Review of vancomycin-induced renal complications. Ther Adv Endocrinol Metab. 2016;7(3):136–47.
  42. Staatz CE, Tett SE. Clinical pharmacokinetics and pharmacodynamics of tacrolimus in solid organ transplantation. Clin Pharmacokinet. 2004;43(10):623–53.
  43. Zinman B, Wanner C, Lachin JM, Fitchett D, Bluhmki E, Hantel S, et al. Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med. 2015;373(22):2117–28.
  44. Neal B, Perkovic V, Mahaffey KW, de Zeeuw D, Fulcher G, Erondu N, et al. Canagliflozin and cardiovascular and renal events in type 2 diabetes. N Engl J Med. 2017;377(7):644–57.
  45. Mehta U, Dheda M, Steel G, Blockman M, Pillay S. Strengthening pharmacovigilance in South Africa. S Afr Med J. 2014;104(2):104–5.
  46. WHO-UMC. The use of the WHO–UMC system for standardised case causality assessment. Uppsala: Uppsala Monitoring Centre; 2005.
  47. Ghiculescu RA. Therapeutic drug monitoring: which drugs, why, when and how to do it. Aust Prescr. 2008;31(2):42–4.
  48. Bates DW, Teich JM, Lee J, Seger D, Kuperman GJ, Ma'Luf N, et al. The impact of computerized physician order entry on medication error prevention. J Am Med Inform Assoc. 1999;6(4):313–21.
  49. NICE. Medicines adherence: involving patients in decisions about prescribed medicines. Clinical Guideline CG76. London: NICE; 2009.
  50. WHO. Patient safety solutions: medication reconciliation. High 5s Initiative. Geneva: WHO; 2007.
  51. Leelakanok N, Holcombe AL, Lund BC, Gu X, Schweizer ML. Association between polypharmacy and death: a systematic review and meta-analysis. J Am Pharm Assoc. 2017;57(6):729–38.
  52. Gnjidic D, Hilmer SN. Pharmacological basis of polypharmacy and ADRs. Clin Pharmacol Ther. 2015;97(5):468–70.
  53. US Food and Drug Administration. Guidance for industry: Drug interaction studies. FDA; 2020.
  54. Phillips EJ, Mallal SA. Pharmacogenetics of drug hypersensitivity. Pharmacogenomics. 2004;5(7):877–89.
  55. Ritter JM, Flower R, Henderson G, Loke YK, MacEwan D, Rang HP. Rang and Dale's Pharmacology. 9th ed. Edinburgh: Elsevier; 2020.
  56. Moore N, Lecointre D, Noblet C, Mabille M. Frequency and cost of serious adverse drug reactions in a department of general medicine. Br J Clin Pharmacol. 1998;45(3):301–8.
  57. Varricchio C, Pierce M, Dolan CB, Johnson KA. A Cancer Source Book for Nurses. 8th ed. Burlington: Jones & Bartlett; 2003.
  58. Meyboom RH, Egberts AC, Edwards IR, Hekster YA, de Koning FH, Gribnau FW. Principles of signal detection in pharmacovigilance. Drug Saf. 1997;16(6):355–65.
  59. Bégaud B, Martin K, Haramburu F, Moore N. Rates of spontaneous reporting of adverse drug reactions in France. JAMA. 2002;288(13):1588.
  60. Hazell L, Shakir SA. Under-reporting of adverse drug reactions: a systematic review. Drug Saf. 2006;29(5):385–96.
  61. Avery AJ, Dex GM, Mulvaney C, Serumaga B, Spencer R, Lester HE, et al. Development of prescribing-safety indicators for GPs using the RAND appropriateness method. Br J Gen Pract. 2011;61(589):e526–36.
  62. Scott IA, Hilmer SN, Reeve E, Potter K, Le Couteur D, Rigby D, et al. Reducing inappropriate polypharmacy: the process of deprescribing. JAMA Intern Med. 2015;175(5):827–34.
  63. WHO-UMC. Pharmacovigilance: ensuring the safe use of medicines. Policy perspectives on medicines No. 009. Geneva: WHO; 2004.
  64. Kalisch LM, Caughey GE, Barratt JD, Gilbert AL, Roughead EE. Prevalence of preventable medication-related hospitalisations in Australia. Med J Aust. 2012;197(2):97–101.
  65. Aronson JK. Medication errors: definitions and classification. Br J Clin Pharmacol. 2009;67(6):599–604.

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Rutuja Anarthe
Corresponding author

Student (College of Pharmaceutical sciences, PIMS-DU,loni.bk, Maharashtra)

Photo
Apeksha Kadu
Co-author

Student (College of Pharmaceutical sciences, PIMS-DU,loni.bk, Maharashtra)

Photo
Rushikesh Gaikwad
Co-author

Student (College of Pharmaceutical sciences, PIMS-DU,loni.bk, Maharashtra)

Photo
Shubham Kanawde
Co-author

Assistant professor - Department of Pharmaceutics, College of Pharmaceutical sciences, PIMS-DU,loni.bk, Maharashtra

Rutuja Anarthe, Apeksha Kadu, Rushikesh Gaikwad, Shubham KanawadeAdverse Drug Reactions and Drug Safety Profiles: A Comprehensive Review of Classification, Mechanisms, Clinical Evaluation, and Pharmacovigilance Strategies, Int. J. of Pharm. Sci., 2026, Vol 4, Issue 5, 5751-5763, https://doi.org/10.5281/zenodo.20341843

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