Abstract
Background
Pharmacogenomic‐guided medication management optimises drug therapy to enhance patient outcomes. Despite clinical utility, implementation in Australia remains limited, partly due to the lack of clear and consistent guidance.
Aim
This study evaluated the presence and consistency of pharmacogenomic testing indication categories and therapeutic recommendations between Australian prescribing resources and international pharmacogenomic guidelines.
Methods
Testing indications were compared between Dutch Pharmacogenetics Working Group (DPWG), Royal College of Pathologists of Australasia (RCPA) and Australian prescribing resources (Australian Medicines Handbook (AMH), Therapeutic Guidelines (TG), Therapeutics Goods Administration (TGA) and eviQ). Therapeutic recommendations were compared between Clinical Pharmacogenetics Implementation Consortium (CPIC), DPWG, and Australian prescribing resources. Weighted kappa was used for analysis (n > 10) and interpreted using the Landis and Koch scale.
Results
Among Australian resources, AMH included the highest proportion of testing indications (28%, 19/67); however, it remains an overall low proportion. Testing indications were most consistent between RCPA and TG, with 50% (5/10, 95% CI 0.19–0.81) concordant (e.g. mercaptopurine/TPMT testing ‘recommended’ in both). For therapeutic recommendations, AMH demonstrated substantial agreement with DPWG (κ 0.80, 95% CI 0.61–0.98); however, 11% (3/27) of recommendations were discordant (e.g. capecitabine/DPYD deficiency ‘dose adjustment’ in DPWG and ‘contraindication’ in AMH). Within Australian resources, most (88%, 7/8, 95% CI 0.47–1.00) testing indications were concordant, while for therapeutic recommendations, just over half (57%, 4/7, 95% CI 0.18–0.90) were concordant.
Conclusions
Australian prescribing resources contain limited and inconsistent pharmacogenomic information. Greater harmonisation is needed to support decision‐making and facilitate broader pharmacogenomic implementation.
Keywords: pharmacogenomics, indications, recommendations, consistency, consistent
Introduction
Pharmacogenomics refers to the influence of genetic variation on drug pharmacokinetics and pharmacodynamics. 1 By predicting an individual's likely drug response, pharmacogenomics can guide drug and dose selection and facilitate precision medicine. 1 Pharmacogenomic‐guided prescribing may improve treatment efficacy and reduce adverse drug events. 2 In Australia, medication‐related hospitalisations account for an estimated 250 000 admissions and approximately 1.4 billion Australian dollars annually, of which about half are potentially preventable. 3 Pharmacogenomic‐guided prescribing has the potential to reduce up to 30% of adverse drug reactions, although the extent of this reduction is likely to be lower in real‐world settings. 1 , 4
Despite its role in supporting prescribing decisions, pharmacogenomics is not widely adopted in Australian healthcare. 5 Barriers to implementation include limited knowledge and uncertainty of the clinical value by healthcare professionals, lack of supportive workflows, testing costs and the absence of national clinical practice guidelines. 5 International consortia such as the Clinical Pharmacogenetics Implementation Consortium (CPIC) and Dutch Pharmacogenetics Working Group (DPWG) develop guidelines with therapeutic recommendations to support the application of pharmacogenomic results. 6 , 7 DPWG additionally provides testing indications. 7 However, Australian healthcare professionals continue to perceive the absence of clear, nation‐specific guidelines as a barrier to implementation. 8 International guidelines are often based on populations of European ancestry, which may limit their relevance to Australia's diverse population. 9 , 10 Local guidelines are therefore important to ensure applicability to Australian drug availability and population‐specific genetic variations. 8
Recently, the Royal College of Pathologists of Australasia (RCPA) (https://www.rcpa.edu.au/Library/Practising-Pathology/Pharmacogenomic-Indications-in-Australia) published testing indications for 38 drug‐gene pairs. 11 While this is an important step to foster implementation, translation of these indications in Australian prescribing resources are lacking. 8 Key Australian prescribing resources, including the Australian Medicines Handbook (AMH), Therapeutic Guidelines (TG), approved product information (PI) from drug sponsors listed by the Therapeutic Goods Administration (TGA) and eviQ (for oncology medicines only), contain limited pharmacogenomic guidance. 12 These resources vary in purpose and development processes. AMH follows an editorial and peer‐reviewed process, while TG and eviQ are evidence‐based resources that explicitly cite the literature that underpins their recommendations. In contrast, the TGA‐approved PI is authored by the pharmaceutical company sponsor and reflects regulatory submission requirements rather than independently synthesised guidance. 13 These systematic differences in governance and transparency may influence how pharmacogenomic evidence is presented.
International inconsistencies in pharmacogenomic recommendations 14 , 15 , 16 , 17 present a challenge for their use by healthcare professionals. 14 It is unclear whether Australian prescribing resources include pharmacogenomic information and whether content aligns with international guidelines or is consistent across other Australian resources.
The objectives of this study were to (1)
document the inclusion of pharmacogenomic testing indications in Australian prescribing resources and (2)
evaluate the consistency of testing indication categories and therapeutic recommendations between Australian resources and international guidelines.
Methods
Data collection
A list of drug‐gene pairs classified as ‘Level A’ with ‘final’ guideline status on the CPIC database was created. This list was cross‐referenced with the Clinical Pharmacogenomics (ClinPGx) database and restricted to pairs with a ClinPGx evidence ‘Level 1A’. The resulting list was refined to only include drugs available in Australia, verified through the Australian Register of Therapeutic Goods database (Fig. 1).
Figure 1.
Drug‐gene pair inclusion and exclusion flowchart. CPIC: Clinical Pharmacogenetics Implementation Consortium; ARTG: Australian Register of Therapeutic Goods.
For each drug‐gene pair, the following data were extracted: (1) drug (e.g. abacavir); (2) therapeutic class (e.g. anti‐infectives); (3) genetic variant (e.g. HLA‐B*57:01 carrier); (4) testing indication (e.g. recommended); (5) therapeutic recommendation (e.g. contraindication); (6) clinical and/or pharmacokinetic implications (e.g. hypersensitivity); and (8) date of latest version update (Tables 2, 3 and S1).
Table 2.
Pharmacogenomic testing indication categories across international (DPWG) and Australian (RCPA) guidelines and key Australian prescribing resources (AMH, TG, TGA and eviQ)
| Drug | Therapeutic class | Gene/allele | DPWG | RCPA | AMH | TG | TGA | eviQ |
|---|---|---|---|---|---|---|---|---|
| Abacavir | Anti‐infectives | HLA‐B*57:01 | ||||||
| Allopurinol | Immunomodulators | HLA‐B*58:01 | * | |||||
| Amikacin | Anti‐infectives | MT‐RNR1 | † | † | ||||
| Amitriptyline | Psychotropic drugs | CYP2C19 | ||||||
| CYP2D6 | ||||||||
| Atazanavir | Anti‐infectives | UGT1A1 | ||||||
| Atomoxetine | Psychotropic drugs | CYP2D6 | ||||||
| Atorvastatin | Cardiovascular | SLCO1B1 | # | |||||
| Azathioprine | Immunomodulators | NUDT15 | ||||||
| TPMT | ||||||||
| Capecitabine | Anti‐cancer drugs | DPYD | ||||||
| Carbamazepine | Neurological drugs | HLA‐A*31:01 | * | |||||
| HLA‐B*15:02 | * | * | * | |||||
| Celecoxib | Immunomodulators | CYP2C9 | ||||||
| Citalopram | Psychotropic drugs | CYP2C19 | ||||||
| Clopidogrel | Blood and electrolytes | CYP2C19 | § | |||||
| Codeine | Analgesics | CYP2D6 | ¶ , # | |||||
| Desflurane | General anaesthesia | CACNA1S | ||||||
| RYR1S | ||||||||
| Efavirenz | Anti‐infectives | CYP2B6 | ¶ , # | |||||
| Escitalopram | Psychotropic drugs | CYP2C19 | ||||||
| Fluorouracil | Anticancer drugs | DPYD | ||||||
| Flurbiprofen | Immunomodulators | CYP2C9 | ||||||
| Fluvastatin | Cardiovascular drugs | CYP2C9 | ||||||
| SLCO1B1 | ||||||||
| Gentamicin | Anti‐infectives | MT‐RNR1 | † | † | ||||
| Ibuprofen | Immunomodulators | CYP2C9 | ||||||
| Isoflurane | General anaesthetics | CACNA1S | ||||||
| RYR1 | ||||||||
| Ivacaftor | Respiratory drugs | CFTR | ||||||
| Lansoprazole | Gastrointestinal frugs | CYP2C19 | ||||||
| Meloxicam | Immunomodulators | CYP2C9 | ||||||
| Mercaptopurine | Anticancer drugs | NUDT15 | ||||||
| TPMT | ||||||||
| Methoxyflurane | General anaesthetics | CACNA1S | ||||||
| RYR1 | ||||||||
| Nortriptyline | Pyshcotropic drugs | CYP2D6 | ||||||
| Omeprazole | Gastrointestinal drugs | CYP2C19 | ||||||
| Ondansetron | Gastrointestinal drugs | CYP2D6 | ||||||
| Oxcarbazepine | Neurological drugs | HLA‐B*15:02 | * | * | * | |||
| Pantoprazole | Gastrointestinal drugs | CYP2C19 | ||||||
| Paroxetine | Psychotropic frugs | CYP2D6 | ||||||
| Peginterferon alfa‐2a | Anti‐infectives | IFNL3 | ||||||
| IFNL4 | ||||||||
| Phenytoin | Neurological drugs | CYP2C9 | ||||||
| HLA‐B*15:02 | * | |||||||
| Piroxicam | Immunomodulators | CYP2C9 | ||||||
| Pitavastatin | Cardiovascular drugs | SLCO1B1 | ||||||
| Pravastatin | Cardiovascular drugs | SLCO1B1 | ||||||
| Rasburicase | Anticancer drugs | G6PD | ||||||
| Rosuvastatin | Cardiovascular drugs | ABCG2 | ||||||
| SLCO1B1 | # | |||||||
| Sertraline | Psychotropic drugs | CYP2C19 | ||||||
| Sevoflurane | General anaesthetics | CACNA1S | ||||||
| RYR1 | ||||||||
| Simvastatin | Cardiovascular drugs | SLCO1B1 | ||||||
| Tacrolimus | Immunomodulators | CYP3A5 | ||||||
| Tamoxifen | Anticancer drugs | CYP2D6 | ||||||
| Thioguanine | Immunomodulators | NUDT15 | ||||||
| TPMT | ||||||||
| Tobramycin | Anti‐infectives | MT‐RNR1 | † | † | ||||
| Tramadol | Analgesics | CYP2D6 | ||||||
| Tropisetron | Gastrointestinal drugs | CYP2D6 | ||||||
| Voriconazole | Anti‐infectives | CYP2C19 | ‖ | |||||
| Vortioxetine | Psychotropic drugs | CYP2D6 | ||||||
| Warfarin | Blood and electrolytes | CYP2C9 | ||||||
| VKORC1 | ||||||||
| CYP4F2 |
Note: 
Recommended; 
Consider; 
Not Recommended; 
Uncertain.
At‐risk ancestral groups.
Family history.
Patient specific risk factors.
Specific indication.
Specific dose regimen.
Clinical response dependent.
Table 3.
Comparison of pharmacogenomic therapeutic recommendations and implications across international pharmacogenomic guidelines (CPIC and DPWG) and Australian prescribing resources (AMH, TG, TGA and eviQ).
|
|
Note: Most up to date information included as of 27 November 2025. For more detail, see Table S1. “No information” indicates that the drug monograph contained no relevant pharmacogenomic information. “No drug monograph” indicates that no monograph for the drug was identified within the resource.
Abbreviations: CI, contraindication; conc., concentration; DA, dose adjustment; FU, fluorouracil; Hs, hypersensitivity; IM, intermediate metaboliser; impl., implication; PM, poor metaboliser; rec., recommendation; SJS, Stevens‐Johnson syndrome; TEN, toxic epidermal necrolysis.
Pharmacogenomic testing indications
Indications for pharmacogenomic testing were identified by reviewing the international DPWG guidelines and the national RCPA guideline, together with Australian prescribing resources (AMH, TG, TGA PI and eviQ). Where present, testing indications were recorded into four categories: (1) recommended, (2) consider, (3) not recommended or (4) uncertain (Table 2).
Recommended – directive language indicating testing is required or strongly advised (e.g. ‘essential’, ‘recommended’, ‘test before prescribing’).
Consider – non‐directive language suggesting testing may be beneficial (e.g. ‘beneficial’, ‘consider’).
Not recommended – explicit statements that testing should not be performed.
Uncertain – statements indicating the test is ‘not validated’, evidence is ‘uncertain’ or there is ‘no consensus’.
Any indications restricted to specific patient populations were also recorded.
Pharmacogenomic therapeutic recommendations
Pharmacogenomic therapeutic recommendations were obtained from the international guidelines, CPIC and DPWG, and from the key Australian prescribing resources. For each drug‐gene pair, the therapeutic recommendation and implication were recorded (Table 3).
Recommendation
Recommendations related to dose selection or adjustment for patients with an actionable genotype were grouped into four ordered categories:
Contraindication – avoid the drug or use an alternative (e.g. ‘alternative drug’, ‘avoid’, ‘contraindicated’).
Dose adjustment – specific or non‐specific dose changes (e.g. ‘reduce dose by 50%’, ‘use 1.7 times standard dose’, or ‘dose reduction’).
Use with caution – no explicit dose change but cautions or enhanced monitoring (e.g. ‘consider lower titration and lower maintenance doses’ or warnings such as ‘possible risk of myopathy with doses > 40 mg/day’).
No dose adjustment no change to standard dosing.
Where multiple recommendations were present (e.g. ‘avoid; if not possible, use 10% of standard dose’) only a single recommendation was recorded per drug‐gene pair to enable consistent comparison. In this case, the strongest recommendation was selected according to the following hierarchy: contraindication > dose adjustment > use with caution > no dose adjustment (adapted from Shugg et al. 2020). 14
Implication
Clinical implications included for analysis were risk of adverse events or clinical consequences associated with the drug‐gene pair (e.g. hypersensitivity, toxicity). Where explicit adverse event information was not provided, pharmacological implications of the genotype on drug pharmacokinetics were recorded (e.g. ‘decreased plasma concentrations’).
Data management and reporting
All extracted data were recorded in a table. To ensure accuracy and consistency, all extracted data were independently reviewed and verified by a second reviewer. Any discrepancies were resolved through discussion and consensus.
Statistical analysis
Descriptive statistics were used to summarise the data. Analyses used Cohen's weighted kappa to assess the consistency between international (CPIC and DPWG) and national (RCPA) pharmacogenomic guidelines and key Australian prescribing resources (AMH, TG, TGA PI and eviQ). Additionally, agreement between two commonly used resources, AMH and TG, were evaluated to assess the consistency within national resources, given that healthcare professionals often consult more than one reference for clinical decision‐making. Interpretation of Cohen's weighted kappa were based on the Landis and Koch scale (1977) 18 (Table 1). Weighted kappa was calculated for comparisons with a sample size greater than 10. 19 Statistical analyses were performed using Microsoft Excel (Version 16.101 (25091314)) and R Console (Version 4.5.1 (8536)).
Table 1.
Landis and Koch scale for Kappa interpretation
| Kappa statistic (κ) | Strength of agreement |
|---|---|
| <0.00 | Poor |
| 0.00–0.20 | Slight |
| 0.21–0.40 | Fair |
| 0.41–0.60 | Moderate |
| 0.61–0.80 | Substantial |
| 0.81–1.00 | Almost Perfect |
Results
Characteristics of drug‐gene pairs and resources
A total of 67 drug‐gene pairs were included. The most frequently represented genes were CYP2D6 (15%, 10/67), CYP2C19 (13%, 9/67), CYP2C9 (12%, 8/67) and SLCO1B1 (9%, 6/67). Across the 53 therapeutic areas represented, the most common were immunomodulators and anti‐inflammatories (17%, 9/53), followed by anti‐infectives (15%, 8/53) and psychotropics (15%, 8/53).
Presence of pharmacogenomic testing indications
Of the 67 drug‐gene pairs included, RCPA provided an indication for 63% (42/67) of drug‐gene pairs, while in key Australian prescribing resources, an indication was provided for 28% (19/67), 19% (13/67), 19% (13/67) and 3% (2/67) in AMH, TG, TGA and eviQ respectively (Table 2). Examples of drug‐gene pairs with no indication in RCPA or key Australian prescribing resources include desflurane/CACNA1S, meloxicam/CYP2C9 and tropisetron/CYP2D6. Among the 47 testing indications provided across key Australian prescribing resources, 28% (13/47) were conditional, being restricted to specific at‐risk ancestral groups (13%, 6/47), positive family history (13%, 6/47) or clinical response dependent (2%, 1/47).
Comparison of pharmacogenomic testing indications across sources
DPWG and RCPA
Of the 67 drug‐gene pairs included, DPWG provided an indication for 52% (35/67) of drug‐gene pairs. A total of 33 (49%, 33/67) drug‐gene pairs had a testing indication listed by both DPWG and RCPA. Over half (58%, 19/33, 95% CI 0.39–0.75) of the indications were concordant. For example, clopidogrel/CYP2C19 testing was ‘recommended’ in both, while paroxetine/CYP2D6 is ‘considered’ in DPWG but ‘uncertain’ in RCPA. There was fair agreement for the indication categories between DPWG and RCPA (κ 0.33, 95% CI 0.17–0.49).
RCPA and key Australian prescribing resources
Across the key Australian prescribing resources, the strength of agreement with RCPA pharmacogenomic testing indications varied considerably.
A total of 15 drug‐gene pairs had a testing indication listed by both RCPA and AMH. Just under half (47%, 7/15, 95% CI 0.08–0.55) of the drug‐gene pairs had indication categories which were concordant (e.g. abacavir/HLA‐B*5701: ‘recommended’ in both), with the remaining discordant (e.g. tamoxifen/CYP2D6: ‘considered’ in RCPA but ‘not recommended’ in AMH). There was fair agreement between RCPA and AMH (κ 0.38, 95% CI 0.22–0.54) across the four indication categories.
A total of 10 drug‐gene pairs had a testing indication listed by both RCPA and TG. Half (5/10, 95% CI 0.19–0.81) of the indication categories were concordant. For example, mercaptopurine/TPMT testing was ‘recommended’ in both, while voriconazole/CYP2C19 was ‘recommended’ in RCPA but ‘considered’ in TG.
A total of 13 drug‐gene pairs had a testing indication listed by both RCPA and TGA. Of these, 31% (4/13, 95% CI 0.09–0.61) of the indication categories were concordant and the rest discordant (e.g. thioguanine/NUDT15: ‘recommended’ in RCPA but ‘considered’ in TGA). There was no agreement between RCPA and TGA indication categories (κ 0.00, 95% CI −1.2 × 10−7–1.2.0 × 10−7).
Only two drug‐gene pairs had a testing indication listed by both RCPA and eviQ. Both indication categories were concordant (e.g. capecitabine/DPYD: ‘recommended’ in both).
Within Australian prescribing guidelines
A total of eight drug‐gene pairs had testing indications listed by both AMH and TG. Most (88%, 7/8, 95% CI 0.47–1.00) indication categories were concordant (e.g. tobramycin/MT‐RNR1: ‘considered’ in both) with a single discordant pair (carbamazepine/HLA‐B*1502: ‘considered’ in AMH but ‘recommended’ in TG).
Comparison of pharmacogenomic therapeutic recommendations across sources
Comparison between international and key Australian prescribing guidelines
When comparing international (CPIC and DPWG) with key Australian prescribing resources, strength of agreement in therapeutic recommendations was generally moderate (Table 3; Table S1).
A total of 31 drug‐gene pairs had a therapeutic recommendation provided in both CPIC and AMH, of which, 77% (24/31, 95% CI 0.59–0.90) were concordant (e.g. azathioprine/NUDT15: ‘dose adjustment’ in both). There was moderate agreement between CPIC and AMH (κ 0.67, 95% CI 0.50–0.83) therapeutic recommendations. Of the 27 drug‐gene pairs with a therapeutic recommendation in both DPWG and AMH, 89% (24/27, 95% CI 0.71–0.98) were concordant (e.g. clopidogrel/CYP2C19: ‘contraindication’ in poor metabolisers). There was substantial agreement between DPWG and AMH (κ 0.80, 95% CI 0.61–0.98) therapeutic recommendations.
A total of 10 drug‐gene pairs had a therapeutic recommendation in both CPIC and TG. Of these, 80% (8/10, 95% CI 0.44–0.97) were concordant (e.g. gentamicin/MT‐RNR1 mutation: ‘contraindication’ in both). Of the seven drug‐gene pairs with a therapeutic recommendation in both DPWG and TG, 43% (3/7, 95% CI 0.10–0.82) were discordant (e.g. mercaptopurine/TPMT deficiency: ‘contraindication’ in DPWG and ‘dose adjustment’ in TG).
A total of 41 drug‐gene pairs had a therapeutic recommendation in both CPIC and TGA. Of these, 76% (31/41, 95% CI 0.60–0.88) were concordant (e.g. rasburicase/G6PD deficiency: ‘contraindication’ in both) and the remaining were discordant (e.g., piroxicam/CYP2C9 poor metabolisers: ‘contraindication’ in CPIC and ‘use with caution’ in TGA). There was moderate agreement between CPIC and TGA (κ 0.48, 95% CI 0.30–0.66) therapeutic recommendations. Of the 27 drug‐gene pairs with a therapeutic recommendation on DPWG and TGA, 85% (23/27, 95% CI 0.66–0.96) were concordant (e.g. fluorouracil/DPYD complete deficiency: ‘contraindication’ in both). There was substantial agreement between DPWG and TGA (κ 0.74, 95% CI 0.54–0.95) therapeutic recommendations.
Only two drug‐gene pairs (capecitabine/DPYD and fluorouracil/DPYD) had a therapeutic recommendation across CPIC, DPWG and eviQ. All therapeutic recommendations were concordant.
Comparison within Australian prescribing guidelines
A total of seven drug‐gene pairs had a therapeutic recommendation in both AMH and TG. Just over half (57%, 4/7, 95% CI 0.18–0.90) were concordant (e.g. abacavir/HLA‐B*5701 carriers: ‘contraindication’ in both), while the rest were discordant (mercaptopurine/TPMT complete deficiency: ‘contraindication’ in AMH and ‘dose adjustment’ in TG).
Discussion
This study evaluated the presence and consistency of pharmacogenomic testing indications and therapeutic recommendations across international and national pharmacogenomic guidelines and Australian prescribing resources. Overall, Australian resources include limited pharmacogenomic testing indications and recommendations, and, when present, consistency with guidelines and other Australian resources is minimal. These factors may be a contributing barrier to the use of pharmacogenomics in practice.
Australian prescribing resources currently have limited inclusion of pharmacogenomic testing indications for drug‐gene pairs known to have clinically actionable variants. While pharmacogenomics is one factor contributing to the variability in drug response, the absence of comprehensive national guidelines contributes as a barrier to clinical adoption. 5 , 8 Recently, RCPA published indications for pharmacogenomic testing, providing accessible guidance for healthcare professionals. 11 However, Australian prescribing resources including AMH and TG are yet to translate these indications into their resources. Although AMH contained the highest number of testing indications, they still only covered less than one‐third of the drug‐gene pairs analysed. When included, the strength of agreement with RCPA categories was weak. Relevantly, RCPA indications were only published recently, so editorial teams of prescribing resources may not have had sufficient opportunity to incorporate pharmacogenomic content within their guidelines. However, given that most AMH and TG drug monographs were updated after the RCPA publication, discordance may be due to non‐specific development processes, editorial priorities, limited awareness of RCPA indications or the perceived relevance of pharmacogenomics. 20 , 21
Interestingly, pharmacogenomic testing indications were most consistent across all Australian resources for the two tests that are currently funded through the Medical Benefits Scheme (MBS): TPMT testing for thiopurine drugs and HLA‐B*57:01 testing for abacavir. 22 The availability of MBS rebates likely reflects broader clinical consensus and established evidence supporting their clinical utility. Despite this, between 2011 and 2022, only 33% of thiopurine users underwent TPMT testing. 23 While testing rates did increase over time, the underutilisation may be suggestive of the need for further education, guidance and awareness for healthcare professionals. Pharmacogenomic testing for DPYD for fluoropyrimidine drugs has only recently been added to the MBS (effective November 2025). 24 While therapeutic recommendations are mostly consistent across all resources, testing indications for DPYD deficiency remains inconsistent. For example, AMH and TG do not include a testing indication, TGA categorises testing as ‘considered’ and eviQ as ‘recommended’. Pharmacogenomic testing of HLA‐A*21:01 and HLA‐B*15:02 for carbamazepine and oxcarbazepine has recently been approved for MBS rebate (effective 2026), 25 yet similar inconsistencies in the categorisation of testing indications persist across Australian resources.
Pharmacogenomic therapeutic recommendations in Australian resources were lacking and only moderately consistent with international standards. Drug PI documents listed with TGA provided the most recommendations. However, unlike AMH and TG, PI documents are not among the first consulted resource for prescribing information by healthcare professionals and may not reflect current accepted practice. 13 Inconsistencies in therapeutic recommendations between Australian prescribing resources and international guidelines and discrepancies within Australian resources were common. For example, mercaptopurine in complete TPMT deficiency is categorised as a ’contraindication’ in AMH but a ’dose reduction’ in TG. Such conflicting recommendations are likely to challenge healthcare professionals and impact the confidence they place in pharmacogenomic‐guided medication management. 14 Harmonisation of pharmacogenomic guidance is essential to minimise inconsistencies and support clinical decision‐making. One approach to achieving this is similar to the approach taken by the Centre of Excellence in Regulatory Science and Innovation in Pharmacogenomics in the United Kingdom, which provides national guidelines for pharmacogenomic testing indications and recommended clinical actions based on test results, 26 the scope of the Australian RCPA can be expanded to also include therapeutic recommendations (along with testing indications).
Although international consortia such as CPIC and DPWG provide valuable pharmacogenomic guidelines, the evidence base underpinning their recommendations are predominantly derived from populations of European ancestry. 9 , 27 Consequently, Australian prescribing resources should adapt these recommendations to ensure they reflect Australia's multiethnic demographics, including individuals of Indigenous ancestry. 10 , 28 For example, the HLA‐B*58:01 variant that is associated with allopurinol‐induced cutaneous adverse reactions is most prevalent among individuals of Han‐Chinese, Korean or Thai descent. 29 From the 2021 Australian census, individuals of Chinese, Korean and Thai ancestry represent approximately 3.2% of the Australian population. 30 While DPWG ‘considers’ testing for HLA‐B*58:01, RCPA ‘recommends’ testing, given the relatively high proportion of at‐risk populations in Australia. Contrastingly, AMH currently ‘considers’ testing and so should be updated to better align with population risks and RCPA guidelines. The lack of diverse population representation in the evidence that underpin international guidelines may partly explain the moderate agreement observed between international and Australian resources in both testing indications and therapeutic recommendations. The integration of pharmacogenomics into Australian prescribing resources could adapt and build upon international frameworks to ensure relevance for Australia's demographic profile and to promote equitable implementation across the Australian healthcare system.
This study has several limitations. Firstly, the categorisation of therapeutic recommendations (e.g. dose adjustment) limited the comparison of descriptive recommendations (e.g. reduce dose by 50%), but Australian resources rarely included descriptive recommendations, so this would not have affected the conclusions. Secondly, the analysis was restricted to drug‐gene pairs with high levels of evidence, which does not capture the full spectrum of drug‐gene pairs with pharmacogenomic guidance included in prescribing resources. Furthermore, the analysis was limited to Australian prescribing resources. Therefore, findings may not fully represent other resources consulted in practice, such as Medicines Complete and BMJ Best Practice. Exclusion of these international resources was deliberate to keep an Australian‐specific focus.
Conclusion
In conclusion, Australian prescribing resources currently provide limited pharmacogenomic testing indications and therapeutic recommendations. Where included, there are inconsistencies between national resources and international guidelines. To facilitate the use of pharmacogenomics in practice, consistent and relevant guidelines endorsed by RCPA should be integrated into prescribing resources such as AMH or TG. Furthermore, guidance should be adapted and built on international guidelines.
Supporting information
Table S1. Comparison of pharmacogenomic therapeutic recommendations and implications across international pharmacogenomic guidelines (CPIC and DPWG) and Australian prescribing resources (AMH, TG, TGA and eviQ). Most up to date information included as of 27 November 2025. For more detail, see Appendix Table A1.
Acknowledgements
Open access publishing facilitated by The University of Sydney, as part of the Wiley ‐ The University of Sydney agreement via the Council of Australasian University Librarians.
Conflict of interest: None.
Funding: No funding was received for this work. R.S is supported by an Ross Brown Scholarship in Pharmacy and a PhD scholarship (Genomics Health Futures Mission; MRF2045096).
Data availability statement
Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
References
- 1. Stocker SL, Polasek TM. Using pharmacogenomics to personalise drug therapy: which drugs, when and how. Aust Prescr 2025; 48: 82–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Chenchula S, Atal S, Uppugunduri CRS. A review of real‐world evidence on preemptive pharmacogenomic testing for preventing adverse drug reactions: a reality for future health care. Pharmacogenomics J 2024; 24: 9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Lim R, Ellett LMK, Semple S, Roughead EE. The extent of medication‐related hospital admissions in Australia: a review from 1988 to 2021. Drug Saf 2022; 45: 249–257. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Magavern EF, Megase M, Thompson J, Marengo G, Jacobsen J, Smedley D et al. Pharmacogenetics and adverse drug reports: insights from a United Kingdom national pharmacovigilance database. PLoS Med 2025; 22: e1004565. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Pearce A, Terrill B, Alffenaar JW, Patanwala AE, Kummerfeld S, Day R et al. Pharmacogenomic testing: perception of clinical utility, enablers and barriers to adoption in Australian hospitals. Intern Med J 2022; 52: 1135–1143. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Yoon DY, Lee S, Ban MS, Jang IJ, Lee S. Pharmacogenomic information from CPIC and DPWG guidelines and its application on drug labels. Transl Clin Pharmacol 2020; 28: 189–198. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Abdullah‐Koolmees H, van Keulen AM, Nijenhuis M, Deneer VHM. Pharmacogenetics guidelines: overview and comparison of the DPWG, CPIC, CPNDS, and RNPGx guidelines. Front Pharmacol 2020; 11: 595219. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Ewasiuk E, Emery J, Reid G, Saya S. Implementing pharmacogenomic testing in Australian general practice: an exploratory qualitative study. Pharmacogenomics 2024; 25: 377–389. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Saulsberry L, Jameson JC, Gibbons RD, Dolan ME, Olopade OI, O'Donnell PH. A National Study among Diverse US populations of exposure to prescription medications with evidence‐based Pharmacogenomic information. Clin Pharmacol Ther 2025; 117: 1793–1802. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Health AIo, Welfare . Profile of Australia's Population. Canberra: AIHW; 2025. [Google Scholar]
- 11. RCPA Revolutionises Precision Medicine with Completion of Pharmacogenomics Project (press release). The Royal College of Pathologists of Australasia 2024.
- 12. Hughes S, Kirkpatrick C, Steinke S, Disanayaka A, Ward R, McLachlan A. Research priorities for Australian pharmacogenomics. Australian Genomics; 2022.
- 13. Day R, Snowden L. Where to find information about drugs. Aust Prescr 2016; 39: 88–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Shugg T, Pasternak AL, London B, Luzum JA. Prevalence and types of inconsistencies in clinical pharmacogenetic recommendations among major U.S. sources. NPJ Genom Med 2020; 5: 48. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Bank P, Caudle K, Swen J, Gammal R, Whirl‐Carrillo M, Klein T et al. Comparison of the guidelines of the clinical pharmacogenetics implementation consortium and the Dutch pharmacogenetics working group. Clinical Pharmacol Therapeut 2018; 103: 599–618. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Luzum JA, Cheung JC. Does cardiology hold pharmacogenetics to an inconsistent standard? A comparison of evidence among recommendations. Pharmacogenomics 2018; 19: 1203–1216. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Lee M, Han JM, Lee J, Oh JY, Kim JS, Gwak HS et al. Comparison of pharmacogenomic information for drug approvals provided by the national regulatory agencies in Korea, Europe, Japan, and the United States. Front Pharmacol 2023; 14: 1205624. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977; 33: 159–174. [PubMed] [Google Scholar]
- 19. McHugh ML. Interrater reliability: the kappa statistic. Biochem Med Zagreb 2012; 22: 276–282. [PMC free article] [PubMed] [Google Scholar]
- 20. Tay CT, Joham AE, Teede HJ. Key standards and principles for developing evidence‐based clinical guidelines: balancing health professional, patient, funder, and government needs. Fertil Steril 2025; 123: 561–568. [DOI] [PubMed] [Google Scholar]
- 21. Atkins B, Briffa T, Connell C, Buttery AK, Jennings GLR. Improving prioritization processes for clinical practice guidelines: new methods and an evaluation from the National Heart Foundation of Australia. Health Res Policy Syst 2023; 21: 26. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Genomics in General Practice. East Melbourne, Vic: The Royal Australian College of General Practitioners; 2022. [Google Scholar]
- 23. Yiu CH, Ianni BD, Fujita K, Tan ECK, Hilmer SN, Lu CY. Utilization and associated factors of TPMT testing among Australian adults receiving thiopurines: a national retrospective data‐linkage study. Pharmacotherapy 2025; 45: 12–19. [DOI] [PubMed] [Google Scholar]
- 24. New Medicare benefits schedule (MBS) item for dihydropyrimidine dehydrogenase (DPYD) genotyping to diagnose or predict fluoropyrimidine (FP)‐induced toxicity. In: Department of Health DaA, editor. MBS Online 2025.
- 25. Department of Health DaA . Application No. 1769—Human leukocyte antigen (HLA) testing for hypersensitivity to carbamazepine and oxcarbazepine. In: Committee MSA. 2025.
- 26. Dello Russo C, Frater I, Kuruvilla R, Lip S, O'Neill H, Burke K et al. CYP2C19 genotype testing for clopidogrel: a guideline developed by the UK Centre of Excellence in regulatory science and innovation in pharmacogenomics (CERSI‐PGx). Br J Clin Pharmacol 2026; 92: 329–347. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Johnson JA, Caudle KE, Gong L, Whirl‐Carrillo M, Stein CM, Scott SA et al. Clinical pharmacogenetics implementation consortium (CPIC) guideline for pharmacogenetics‐guided warfarin dosing: 2017 update. Clin Pharmacol Ther 2017; 102: 397–404. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Samarasinghe SR, Hoy W, Jadhao S, McMorran BJ, Guchelaar HJ, Nagaraj SH. The pharmacogenomic landscape of an indigenous Australian population. Front Pharmacol 2023; 14: 1180640. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Dean L, Kane M. Allopurinol therapy and HLA‐B*58:01 genotype. In: Pratt VM, Scott SA, Pirmohamed M, Esquivel B, Kattman BL and Malheiro AJ, eds Medical Genetics Summaries. Bethesda (MD): National Center for Biotechnology Information (US); 2012. [PubMed] [Google Scholar]
- 30. Census Country of birth QuickStats (Internet) . 2021. (cited 18 November 2025). Available from URL: https://www.abs.gov.au/census/find-census-data/search-by-area.
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1. Comparison of pharmacogenomic therapeutic recommendations and implications across international pharmacogenomic guidelines (CPIC and DPWG) and Australian prescribing resources (AMH, TG, TGA and eviQ). Most up to date information included as of 27 November 2025. For more detail, see Appendix Table A1.
Data Availability Statement
Data sharing not applicable to this article as no datasets were generated or analysed during the current study.