Knowledge and perceptions of pharmacogenomics and personalized medicine among pharmacy students in Gujarat: a cross-sectional survey

Abstract

Pharmacogenomics (PGx) plays an important role in personalized medicine by explaining genetic variability in drug response and enabling personalized therapy. Although global efforts have integrated PGx into pharmacy curriculum, in India structured education and training regarding PGx still remain limited. Understanding pharmacy students’ knowledge, perceptions, and attitudes toward PGx is essential for preparing the future workforce for clinical application in healthcare area. A cross-sectional questionnaire-based survey was conducted among undergraduate and postgraduate pharmacy students at the School of Pharmacy, Gujarat Technological University, Gandhinagar, India, in July 2025. Participants were recruited using a convenience sampling approach, and the survey was administered online via google form using a structured and validated questionnaire. The instrument assessed demographic characteristics, knowledge of pharmacogenomics, attitudes toward its clinical application, and sources of information. Data were analysed using SPSS version 25. A total of 200 pharmacy students participated in the survey. Most students demonstrated good conceptual knowledge of pharmacogenomics, with 92.5% correctly identifying PGx as the study of gene–drug interactions and 82.5% recognizing its role in reducing adverse drug reactions. However, awareness of practical aspects was limited: only 38.5% were aware of pharmacogenomic guidelines such as CPIC, PharmGKB, FDA labelling, and 29% reported prior formal instruction on pharmacogenomics in their curriculum. Despite these knowledge gaps, students expressed highly positive attitudes toward pharmacogenomics. Correlation analysis showed a weak to moderate positive association between knowledge and attitude scores (r = 0.37, p < 0.01), indicating that higher knowledge levels were associated with more favourable attitudes toward pharmacogenomics. This study showed that pharmacy students of Gujarat has a strong conceptual understanding of pharmacogenomics and recognizing its role in gene-drug interactions and the reduction of adverse drug reactions. However, awareness was limited which strongly indicates a gap between theoretical knowledge and practical application. Overall it highlights both existing knowledge of students and the need to enhance practical applicability of pharmacogenomics for the future integration of personalized medicine into clinical practice.

Introduction

Pharmacogenomics (PGx), which investigates the interindividual genetic differences that influence reactions to medications, serves as the backbone of personalized medicine. Changes in genes encoding drug metabolizing enzymes, drug transporters, and even drug targets can affect a drug’s pharmacokinetics and pharmacodynamics (Relling and Evans 2015). For example, polymorphisms within the cytochrome P450 family, especially with CYP2D6, CYP2C19, and CYP3A5. (Rau et al. 2009) These include analgesics, proton pump inhibitors, antidepressants, plus anticoagulants. With genetic testing for these variations, clinicians are able to predict therapeutic responses and Adverse Drug Reactions (ADR) including proactively tailoring therapy based on genetic profiles. (Guo et al. 2005) Guidelines have been established by Clinical Pharmacogenetics Implementation Consortium (CPIC) and other comparable organizations aimed at aiding physicians utilizing pharmacogenetic information for rational prescription decision making. (Caudle et al. 2017). Although efforts towards incorporating PGx into clinical settings have increased internationally and there is still a lack of acceptance due to insufficient knowledge among health care providers like pharmacists as well as general readiness on their part. (Haga et al. 2012)

As the precision medicine industry grows, it is likely that pharmacists will be expected to interpret pharmacogenomic (PGx) data and recommend genetically informed therapies. In India, however, the incorporation of pharmacogenomics into pharmacy education is at the earliest stages. With rapid innovations taking place in this area, there is a pressing need to integrate pharmacogenomics into pharmacy curriculum. Unlike in the United States where The Accreditation Council for Pharmacy Education (ACPE) issued directives integrating pharmacogenomics into PharmD programs, developing countries like India are still grappling with inadequate and superficial pharmacogenomics instruction. (ACPE guideline, 2016, Kras Kuzelicki et al. 2019) This lack of uniformity in PGx education directly hampers the adoption of personalized medicine. In addition, several investigations have documented educational gaps in pharmacogenomics among pharmacy students and practicing pharmacists, highlighting insufficient confidence in interpreting pharmacogenomic test results and applying them to patient care (McCullough et al. 2011; Hicks et al. 2015). Even in regions where pharmacogenomics instruction is present, the number of dedicated courses and practical training opportunities remains limited, which may hinder effective integration of genomic medicine into pharmacy practice. Other studies have shown that while students possess limited knowledge about pharmacogenomics, they tend to view it positively which indicates that there is an unaddressed educational gap. (Arafah et al. 2022; Caraballo et al. 2017)

India’s population size combined with its diverse genetics makes pharmacogenomics more accessible over here. As future professionals, pharmacy students must learn about the scientific, ethical, and clinical impacts pharmacogenomics poses on the society. International studies show that pharmacy students generally have favourable opinions towards pharmacogenomics; however, actual knowledge and experience differ greatly from the positive perceptions held. (Caraballo et al. 2017; Wondrasek et al. 2024)

Despite growing global recognition of pharmacogenomics in clinical practice, its integration into healthcare education remains uneven. In several developing healthcare systems, including India, pharmacogenomics education is still evolving, with reports indicating limited curricular coverage, insufficient clinical training, and minimal exposure to genomic technologies in pharmacy and medical education programs (McCullough et al. 2011; Haga et al. 2012). Furthermore, international surveys of pharmacogenomics education have highlighted substantial variability in how pharmacogenomics is incorporated into academic curricula, with many institutions reporting inadequate emphasis on practical implementation and clinical decision-making (Kras Kuželički et al. 2019).

To fill these educational inadequacies there should be an assessment of Indian pharmacy students and their literacy regarding pharmacogenomics. Gaining awareness regarding the lack of information reputation pharmacists hold can help educators and regulators tailor appropriate frameworks aimed at building academic structures matching healthcare needs around the world. This study aims to bridge this gap by assessing pharmacy students’ knowledge and attitudes toward pharmacogenomics for integrating it into clinical practices and decision-making processes.

Materials and methods

Study design

A cross sectional questionnaire based academic survey was conducted among undergraduate and postgraduate pharmacy students of School of Pharmacy, Gujarat Technological University, Gandhinagar, Gujarat, India. The study was conducted in July 2025. This study was carried out in accordance with the ethical standards and approved from the institutional ethics committee of Riddhi Medical Nursing Home with registration number ECR/886/Inst/GJ/2016/rr-24 and with the Helsinki Declaration of 1975, as revised in 2000 (5). Consent was obtained from all students for being included in the study.

Study population and sampling

Approximately 200 students studied at School of Pharmacy were eligible to participate in the study. Eligible participants included students enrolled in the Bachelor of Pharmacy (B. Pharm) program from first to fourth academic year and students enrolled in the Master of Pharmacy (M. Pharm) postgraduate program during the study period. A convenience sampling was used to recruit the students. The survey link was circulated electronically via google form and students were participated voluntarily. Inclusion criteria involved currently enrolled pharmacy students who provided informed consent. A total of 200 students completed the questionnaire, yielding a response rate of approximately 50%. Students were excluded if they were declined to provide informed consent, or submitted incomplete questionnaires. Only fully completed responses were included in the final analysis.

Sample size calculation

The minimum required sample size was calculated using the Raosoft Sample Size Calculator at a 95% confidence level and a 5% margin of error. Considering an eligible population of approximately 200 pharmacy students, the calculated minimum sample size was 196 participants (Lwanga and Lemeshow 1991).

Survey instrument development

A structured, self-administered and validated questionnaire was developed based on previous literature and expert opinion from this area (Boateng et al. 2018). Relevant literature was reviewed for the development of questionnaire items. The questionnaire consisted of four sections: Section A: Demographics (age, gender, year of study, institution); Section B: Knowledge-related questions on pharmacogenomics and personalized medicine (e.g., basic concepts, applications, benefits), Section C: Perceptions and attitudes towards pharmacogenomics, its clinical importance, and ethical considerations, Section D: Sources of knowledge and willingness to receive further education on the topic. The questionnaire was developed based on previously published literature and reviewed by three subject experts from pharmacology area to ensure content validity (Soueid et al. 2024; Al-Suhail et al. 2024). A pilot study was conducted among 20 pharmacy students to assess clarity and feasibility. Internal consistency reliability of the questionnaire was evaluated using Cronbach’s alpha coefficient, which demonstrated acceptable reliability (α = 0.82).

KAP score construction and interpretation

Knowledge, Attitude, and Perception scores were calculated based on responses to the structured questionnaire. For knowledge related items responses were coded as 1 = “I know,” 2 = “I am not sure,” and 3 = “I do not know.” For attitude related items, responses were coded as 1 = “Yes,” 2 = “Not sure,” and 3 = “No.” Individual item scores were summed to generate composite scores for each domain. Lower composite scores indicated better knowledge and more positive attitudes toward pharmacogenomics, whereas higher scores indicated lower awareness or greater uncertainty. These composite scores were used as continuous variables for Pearson correlation analysis and multivariate regression analysis to examine relationships between knowledge, attitudes, and demographic characteristics. Further participants with a Knowledge score at or below the median (≤ 17 out of a possible range of 11–33) were classified as “Good Knowledge” (coded 1), while those above the median were classified as “Poor Knowledge” (coded 2). Similarly, Attitude scores at or below the median (≤ 7 out of a possible 4–13) were classified as “Good Attitude,” and Perception scores at or below the median (≤ 5 out of a possible 3–9) were classified as “Good Perception.” These variables were used as dependent variables in the regression models.

Data collection procedure

The survey was administered using Google Forms (online mode) to ensure wider participation. Participants were briefed about the objectives of the study and provided with an informed consent form. Participation was voluntary, and anonymity was maintained throughout the data collection process. To reduce the possibility of duplicate responses, the questionnaire was configured to accept only one response per participant. Responses were collected anonymously to ensure confidentiality. During data cleaning, incomplete questionnaires were excluded, and only fully completed responses were included in the final analysis.

Data analysis

Collected data were entered into Microsoft Excel and analysed using Statistical Package for the Social Sciences (SPSS) version 25. Descriptive statistics such as frequencies, percentages, mean, and standard deviation were used to summarize demographic and response data. Inferential statistics, including Chi-square tests and ANOVA, were used to assess associations between knowledge/perception scores and demographic variables. Pearson correlation was conducted using KAP scores as continuous variables to examine the inter domain relationship. For multivariate logistic regression analysis composite KAP scores were dichotomized at the median into “Good” versus “poor” categories and served as dependent variables to identify independent predictors. p-value of less than 0.05 was considered statistically significant.

Result

Demographic characteristics

A total of 200 responses were obtained from participants who completed the online questionnaire. Among the respondents 113 were female (56.5%) and 87 were males (43.5%). The majority of participants (80%) were aged 18–20 years. The majority of response were received from second year B. Pharm students (31.0%, n = 62) followed by third year students (29.2%, n = 58) and fourth year students (16.5%, n = 33) and M. Pharm post graduate students (16.5%, n = 33). A smaller proportion of respondents were first year B. Pharm students. The demographic characteristics of the participants are summarised in Table 1.

Table 1.

Demographic Characteristics of Students (n = 200)

Variable	Categories	Frequency (n, %)
Gender	Male	87 (43.5)
	Female	113 (56.5)
Academic Program	B. Pharm	167 (83.5)
	M.Pharm	33 (16.5)
Academic Year	B. Pharm (First Year)	13 (6.5)
	B. Pharm (Second Year)	62 (31.0)
	B. Pharm (Third Year)	58 (29.0)
	B. Pharm (Fourth Year)	33 (16.5)
	M.Pharm Post Graduate	33 (16.5)

Knowledge of PGx

The knowledge and level of awareness of pharmacogenomics and personalized medicine among the participants were assessed using 11 structured questions (Table 2). A large proportion of respondents (92.5%) correctly identified pharmacogenomics as the study of how gene affects an individual’s response to drug’s (Mean ± SD: 1.11 ± 0.39). Similarly, 81.5% recognized that personalized medicine uses genetic information to tailored drug therapy for individual patient and 82.5% agreed that pharmacogenomic can help to reduce adverse drug reaction.

Table 2.

Knowledge and Awareness Regarding Pharmacogenomics among Students (n = 200)

No.	Question	I Know	I don’t Know	I am not Sure	Mean ± SD
1	Pharmacogenomics is the study of how genes affect a person’s response to drugs.	185 (92.5)	9 (4.5)	6 (3.0)	1.11 ± 0.39
2	Personalized medicine uses genetic information to tailor drug therapy for individual patients.	163 (81.5)	10 (5.0)	27 (13.5)	1.32 ± 0.70
3	Do you think pharmacogenomics can help reduce adverse drug reactions?	165 (82.5)	12 (6.0)	23 (11.5)	1.29 ± 0.66
4	Are you aware of any drugs that are prescribed based on a patient’s genetic makeup?	97 (48.5)	103 (51.5)	NA	1.52 ± 0.52
5	Are you aware of any pharmacogenomic guidelines (e.g., CPIC, PharmGKB, FDA labelling)?	77 (38.5)	60 (30.0)	63 (31.5)	1.93 ± 0.84
6	Have you studied pharmacogenomics or personalized medicine as part of your curriculum?	58 (29)	110 (55.0)	32 (16.0)	1.87 ± 0.66
7	Do you know genetic variation of individual can account for 95% variability in drug effects and disposition?	94 (47.0)	44 (22.0)	62 (31.0)	1.84 ± 0.87
8	Do you know the drug’s package insert contains a warning regarding altered metabolism in individuals with certain genetic variants?	110 (55.0)	61 (30.5)	29 (14.5)	1.60 ± 0.73
9	Do you know pharmacogenomic testing is available for most of the medications?	82 (41.0)	72 (36.0)	46 (23.0)	1.82 ± 0.78
10	Do you have any idea about Inter-ethnic variation in the drug response, Inter-individual variation in pharmacokinetic parameters or pharmacodynamic parameters due to genetic variation?	93 (46.5)	61 (30.5)	46 (23.0)	1.77 ± 0.80
11	Do you know Drug response for each individual can be predicted by using Genetic Biomarkers?	128 (64.0)	43 (21.5)	29 (14.5)	1.51 ± 0.73

However, awareness of practical aspects of pharmacogenomics was comparatively lower. Only 48.5% of the respondents were aware that certain drugs are prescribed based on patient’s genetic profile (1.52 ± 0.52), and only 38.5% were aware of established pharmacogenomic guidelines like CPIC, PharmGKB, or FDA labelling (1.93 ± 0.84). Formal curricular exposure was also limited with 29% stating that they had learned pharmacogenomics or personalized medicine in their coursework (1.87 ± 0.66).

Additionally, 47.0% of respondents were aware that genetic variability can contribute to approximately 95% of variability in drug effects and disposition (1.84 ± 0.87). Awareness regarding pharmacogenomic information in drug package inserts was reported by 55% of respondents (1.60 ± 7.31).

Overall while conceptual knowledge of pharmacogenomics was relatively high, awareness of clinical guidelines, testing avaibility and practical implementation remains limited.

Attitude towards pharmacogenomics

Participant’s attitudes towards the integration and implementation of pharmacogenomics were determined using four important questions (Table 3). A majority of participants (85%) supported the broader inclusion of pharmacogenomics in the pharmacy curriculum (Mean ± SD: 1.27 ± 0.66). Additionally, 86.5% expressed interested in receiving further training or attending workshops related to pharmacogenomic (1.14 ± 0.34).

Table 3.

Attitude Regarding Pharmacogenomics among Students (n = 200)

No.	Question	Yes	No	Not Sure	Mean ± SD
1	Do you think pharmacogenomics should be included in the pharmacy curriculum more extensively?	170 (85)	6 (3.0)	24 (12.0)	1.27 ± 0.66
2	Would you be interested in receiving training or attending workshops on pharmacogenomics?	173 (86.5)	27 (13.5)	NA	1.14 ± 0.34
3	Do you think that pharmacogenomic testing will help to decrease the number of Adverse Drug Reactions?	152 (76.0)	12 (6.0)	36 (18.0)	1.42 ± 0.77
4	Do you think Pharmacogenomic Testing help to decrease the cost of development of new drugs?	75 (37.5)	35 (17.5)	90 (45.0)	2.08 ± 0.90

Regarding the perceived clinical benefit of pharmacogenomic testing, 76% of the respondents agreed that it could help reduce the incidence of adverse drug reactions (1.42 ± 0.77). However, opinions differed regarding the economic impact of pharmacogenomic. Only 37.5% believed that pharmacogenomic testing could reduce the cost of drug development, whereas 17.5% disagreed and 45% were uncertain.

These findings suggest strong support among students for the integration of pharmacogenomics into pharmacy education and a high level of interest in further training, although uncertainty remains regarding its economic implications.

Perception and source of information

The internet (37.5%) was reported as the most common source through which students first learned about pharmacogenomics followed by classroom teaching (34%), books/journals (10.5%), and friends/peers (10.5%). Workshops and seminars accounted for 7.5% of first learning experiences.

When respondents were questioned on the best way to enhance student knowledge in this field, the most commonly recommended strategy was curriculum integration (30%), followed closely by guest lectures or workshops (27%) and research projects (27%). Online courses were favoured by 14%, with 2% recommending other strategies.

Regarding future clinical practice 71% of respondents agreed that healthcare practitioners will increasingly use pharmacogenomic tests for medicine and therapy management. In contrast, 7% disagreed and 22% remained neutral (Table 4).

Table 4.

Perception and Source of Information Regarding Pharmacogenomics among Students (n = 200)

No.	Question	Categories	Frequency (%)
18	How did you first learn about pharmacogenomics/personalized medicine?	Classroom teaching	68 (34%)
		Internet	75 (37.5%)
		Books/Journals	21 (10.5%)
		Workshops/Seminars	15 (7.5%)
		Friends/Peers	21 (10.5%)
19	What do you think is the best way to improve student knowledge in this area?	Curriculum integration	60 (30%)
		Guest lectures/workshops	54 (27%)
		Online courses	28 (14%)
		Research projects	54 (27%)
		Others	4 (2.0%)
20	Do you think in future health care providers will use pharmacogenomic tests for medication and therapy management?	Agree	142 (71.0%)
		Disagree	14 (7.0%)
		Neutral	44 (22.0%)

Correlation Analysis: Correlation analysis between knowledge, attitude and practice scores (KAP) shown in Table 5. Knowledge scores were positively correlated with attitude scores (r = 0.37, p < 0.01) and practice scores (r = 0.34, p < 0.01). These results indicate a weak to moderate but statistically significant positive relationship suggesting that students with higher knowledge scores tended to also have more positive attitude and perceptions score among students. Composite knowledge, attitude, and perception scores derived from the questionnaire responses were used as continuous variables for correlation analysis and as median dichotomized binary variables for the logistic regression models.

Table 5.

Correlation among KAP variables

	K Score	A Score	P Score
K Score	1	0.37*	0.34*
A Score	0.37*	1	0.32*
P Score	0.34*	0.32*	1

*Correlation significant at 0.01 level of significance

Multivariate logistic regression analysis was performed to identify predictors associated with knowledge, attitude, and practice outcomes (Table 6) and key predictors are illustrated in Fig. 1. The Fig. 1 presents the odds ratios and 95% confidence intervals for variables included in the regression model. Gender were significantly associated with knowledge levels, with female students demonstrated significantly different knowledge levels compared with male students (OR = 0.22, 95% CI: 0.09–0.55, p = 0.001).

Table 6.

Multivariate Logistic Regression Model for Factors Identification of Good KAP

Variables	Categories	Knowledge		Attitude		Practice
		Coef. (p-value)	OR [95% CI]	Coef. (p-value)	OR [95% CI]	Coef. (p-value)	OR [95% CI]
Gender	Female	-1.517 (0.001)	0.22 [0.09–0.55]	-0.069 (0.851)	0.93 [0.45–1.93]	-0.100 (0.809)	0.91 [0.40–2.03]
	male	Ref	-	-	-	-	-
Do you have any idea about Inter-ethnic variation in the drug response, Inter-individual variation in pharmacokinetic parameters or pharmacodynamic parameters due to genetic variation?	I don’t know	-0.726 (0.226)	0.48 [0.15–1.57]	-0.316 (0.497)	0.73 [0.29–1.81]	0.626 (0.171)	1.87 [0.76–4.59]
	Not sure	-1.866 (0.002)	0.15 [0.05–0.51]	0.188 (0.681)	1.21 [0.49–2.96]	1.050 (0.028)	2.86 [1.12–7.29]
	I know	Ref	-	-	-	-	-
Do you know Drug response for each individual can be predicted by using Genetic Biomarkers?	I don’t know	-2.556 (0.000)	0.08 [0.03–0.21]	-1.165 (0.008)	0.31 [0.13–0.74]	-0.334 (0.490)	0.72 [0.28–1.85]
	I know	Ref	-	-	-	-	-
How did you first learn about pharmacogenomics/personalized medicine?	Internet	-0.531 (0.314)	0.59 [0.21–1.65]	0.693 (0.094)	2.00 [0.89–4.50]	-0.264 (0.508)	0.77 [0.35–1.68]
	Books/Journals	-0.671 (0.377)	0.51 [0.12–2.26]	1.008 (0.130)	2.74 [0.74–10.09]	-1.111 (0.076)	0.33 [0.10–1.12]
	Workshop/Seminar	-1.004 (0.221)	0.37 [0.07–1.83]	-0.849 (0.209)	0.43 [0.11–1.61]	-28.053 (1.000)	0.00 [0.00–inf]
	Friends/Peers	0.299 (0.709)	1.35 [0.28–6.51]	-0.496 (0.461)	0.61 [0.16–2.28]	-26.858 (1.000)	0.00 [0.00–inf]
	Classroom Teaching	Ref	-	-	-	-	-

Fig. 1.

Forest Plots for Predictors of Knowledge, Attitude and Practice

Students who were unaware that genetic biomarkers can predict drug response showed significantly lower knowledge levels (OR = 0.08, 95% CI: 0.03–0.21, p < 0.001) and were also less likely to have positive attitudes toward Pharmacogenomics (OR = 0.31, 95% CI: 0.13–0.74, p = 0.008).

Interestingly, uncertainty regarding inter-ethnic variation in drug response was positively associated with higher practice scores (OR = 2.86, 95% CI: 1.12–7.29, p = 0.028) (Fig. 1).

Discussion

This survey-based study evaluated the knowledge, attitudes and perceptions of pharmacogenomics (PGx) among pharmacy students of Gujarat state. The result suggests that students possess a relatively strong conceptual understanding of pharmacogenomics but demonstrate limited awareness of clinical implementation tools such as pharmacogenomic guidelines and genetic testing resources. At the same time, students expressed highly positive attitudes toward integrating pharmacogenomics into pharmacy education.

Most students identified the fundamental concepts of pharmacogenomics and recognized its role in reducing adverse drug reactions. These findings are consistent with previous international studies reporting that pharmacy students often possess basic theoretical knowledge of PGx but lack practical familiarity with clinical guidelines and implementation strategies (McCullough et al. 2011; Caudle et al. 2017). Similarly, a global survey by Kras Kuželički et al. (2019) found that although pharmacogenomics awareness among healthcare students is increasing, gaps remain in translating theoretical knowledge into clinical practice (Al-Amri et al. 2024).

Although participants demonstrated strong conceptual knowledge of pharmacogenomics, few had formal exposure to it in their curriculum. Low familiarity with key resources like CPIC guidelines and PharmGKB databases indicates that education prioritizes theory over practical clinical application (Al-Suhail et al. 2024).

Students demonstrated highly positive attitudes toward pharmacogenomics education, with a large majority supporting its integration into pharmacy curricula and expressing interest in further training opportunities. Similar findings have been reported in studies conducted in Saudi Arabia and India, where pharmacy students consistently show strong interest in pharmacogenomics education despite limited formal training (Albassam et al. 2018; Kumar et al. 2019). Students’ positive attitudes toward pharmacogenomics (PGx) education likely stem from increasing awareness of personalized medicine’s role in healthcare. Yet, their uncertainty about the economic effects of PGx testing points to limited exposure to its wider pharmacoeconomic and healthcare implications. These findings emphasize the need to bolster pharmacogenomics education in pharmacy programs. International models show that case-based learning, interdisciplinary approaches, and clinical decision-making exercises enhance students’ ability to apply genomic data in patient care (Rahma et al. 2021). Broadening such opportunities could narrow the divide between theory and real-world practice for future pharmacists (Zhou 2018).

The multivariate logistic regression analysis showed that several notable predictors of KAP outcomes which positively integrates with the available literature. Female students showing lower odds of being classified as having good knowledge compare to male students (OR = 0.22, 95% CI, 0.09–0.55, P = 0.001) which indicates Gender was significantly associated with knowledge outcomes. This finding is differing from some prior studies reporting no significant gender difference in pharmacogenomics knowledge (Arafah et al. 2022), while others have noted gender-related variation in health literacy and self-reported confidence in genomic concepts (Albassam et al. 2018). This observed association may reflect differences in self-assessed confidence rather than true knowledge gaps, and require a broader investigation. Students are having unawareness that genetic biomarkers able to predict drug response was strongly associated with lower odds of good knowledge (OR = 0.08, 95% CI: 0.03–0.21, p < 0.001) and less attitude outcomes (OR = 0.31, 95% CI: 0.13–0.74, p = 0.008). These finding gives a conceptual gap about understanding the role of genetic biomarkers in personalised medicine, could be a priority target for curriculum reinforcement. The association between uncertainty about inter-ethnic variation in drug response and higher perception scores (OR = 2.86, 95% CI: 1.12–7.29, p = 0.028) which may reflect that students who acknowledge uncertainty in complex pharmacogenomics concepts demonstrate a broader awareness of the field’s clinical implications, even if their knowledge remains incomplete. A similar pattern has been observed in other health literacy studies, where partial awareness of complexity is associated with greater engagement with a topic (Kras Kuželički et al. 2019).

However, design of our study and the binary outcome construction limit the conclusion. Importantly regression models identified statistically significant predictors but gives limited proportion of overall variance in KAP outcomes. There is a need of further studies with larger sample size and more in depth predictors which would provide a comprehensive understanding of pharmacogenomic knowledge and attitudes among pharmacy students.

Conclusion

This survey-based study provides insights into the current state of pharmacogenomics knowledge and attitudes among pharmacy students in Gujarat. Although the majority of students show a strong conceptual knowledge of pharmacogenomics, there is still a lack of knowledge regarding clinical implementation resources and real-world applications. Simultaneously, student’s high desire for additional training indicates significant opportunity for improving pharmacogenomics instruction in pharmacy curriculum. The observed positive association between knowledge and attitudes score suggests that students with greater pharmacogenomic knowledge also tends to hold more positive attitudes, however due to cross sectional design no causal inference can be drawn. However, the results should be interpreted cautiously due to the convenience sampling strategy and single-institution design. Broader multi-institutional studies are needed to better understand educational gaps and inform future strategies for strengthening pharmacogenomics training in pharmacy education.

Strength and limitation of study

This survey-based study provides a structured assessment of pharmacogenomics knowledge, attitudes, and perceptions among pharmacy students. One strength of the study lies in the use of a systematically developed questionnaire, which was based on existing literature and underwent expert review and pilot testing to ensure content clarity and validity. The questionnaire also demonstrated acceptable internal consistency reliability. Additionally, the study employed multiple analytical approaches, including descriptive statistics, correlation analysis, and multivariate regression, enabling a more comprehensive exploration of the relationships between knowledge, attitudes, and related factors. The inclusion of students from multiple academic years and postgraduate programs also provided a broader perspective on pharmacogenomics awareness within the institution.

However, several limitations should be considered. The cross-sectional design limits the ability to infer causal relationships between knowledge, attitudes, and educational exposure. Furthermore, the study was conducted at a single academic institution using convenience sampling, which may limit the generalizability of the findings to other pharmacy schools or regions. Differences in institutional curricula, educational resources, and regional contexts may influence students’ knowledge and perceptions of pharmacogenomic. The reliance on self-reported responses may also introduce potential response biases, including recall bias and social desirability bias.

Overall, while the study offers preliminary insights into pharmacogenomics awareness among pharmacy students in the study setting, broader studies are required to obtain a more representative understanding of pharmacogenomics education in India.

Additionally, the inclusion of individual knowledge-related questionnaire items (e.g., awareness of genetic biomarkers and inter-ethnic variation) as predictors in regression models where the composite knowledge score was the outcome variable introduces a degree of conceptual overlap, since these items partially contribute to the outcome being modelled. While predictors were selected to reflect conceptually distinct aspects of awareness, future studies should consider using fully independent predictor and outcome domains to eliminate this potential source of circularity. Furthermore, the logistic regression models explain only a limited proportion of variance in KAP outcomes, reflecting the multifactorial nature of pharmacogenomics literacy; unmeasured variables such as prior research experience, self-directed learning habits, and curriculum quality are likely to contribute additional explanatory power.

Overall, while the study offers preliminary insights into pharmacogenomics awareness among pharmacy students in the study setting, broader studies are required to obtain a more representative understanding of pharmacogenomics education in India.

Author contributions

B.M., K.T. and S.C. conceptualized the study. B.M. & K.T. designed the study, collected data, analysed the data, and prepared the first draft manuscript; S.C. supported data collection, and critically reviewed the manuscript. All authors read and approved the final manuscript.

Funding

No funds, grants, or other support were received during the preparation of this manuscript.

Data availability

No datasets were generated or analysed during the current study.

Declarations

This study was carried out in accordance with the ethical standards and approved from the institutional ethics committee of Riddhi Medical Nursing Home with registration number ECR/886/Inst/GJ/2016/rr-24 and with the Helsinki Declaration of 1975, as revised in 2000 (5). Consent was obtained from all students for being included in the study.

Financial disclosure

The authors declare that there are no additional disclosures to report.

Future directions

Future research in this area should broaden to multi institutional across India to capture differences in PGx education based on region. Long-term studies may also help evaluate how new PGx modules affect student knowledge and attitudes. Collaborative efforts between academic institutions, regulatory bodies, and healthcare organizations may also support the development of more effective pharmacogenomics training frameworks for future pharmacists.

Competing interests

The authors declare no competing interests.