Department of Pharmacy
Permanent URI for this communityhttps://repository.cuilahore.edu.pk/handle/123456789/23
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Item Knowledge, Attitude, and Practices of Undergraduate Students Regarding the Use of Mobile Health Applications for the Management and Self-Monitoring of Smog-Related Diseases(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Maha Zulfiqar; CIIT/FA23-RPY-002/LHR; Dr. GhulamMurtaza; LHR TP 9834Globally, smog is one of the major threats to public health. Through the use of smog-related mHealth apps, this study aimed to assess knowledge, attitudes, and preventive measures about smog in undergraduate students from two separate institutions in two different cities in Pakistan. A self-administered questionnaire consisting of 29 items was used to conduct a cross-sectional survey among 376 respondents. Chi-square was used for data analysis. According to the results, apps for mHealth related to smog are still surprisingly underutilized. Just 26.59% of students reported using a smog-related mHealth app in the previous year, and only 34.04% were aware that such apps even existed. Knowledge and usage of mHealth apps were significantly (p<0.05) correlated with demographic traits, including institutional affiliation and gender. Although 37.2% of students believed that the data from mHealth apps was reliable, a noteworthy 43.6% of them had ambiguous sentiments. This lack of interest could be the consequence of uncertainty regarding the mHealth apps' efficiency or unfamiliarity with them. Taking everything into account, this study highlights the importance of integrating mHealth tools into comprehensive public health policies aimed at addressing environmental health emergencies. This can be accomplished through improving app usability, developing targeted awareness campaigns inside educational institutions, and enhancing digital literacy. mHealth apps may prove to be useful tools in the fight against health issues brought on by smog.Item Predicting Clinical Pharmacokinetics of Docetaxel in Cancer Patients Using an Advanced Kinetic Approach(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Sadaf Yaseen; CIIT/FA23-RPY-004/LHR; Dr. Muhammad Ihtisham Umar; LHR TP 9835The pharmacokinetics of the commonly used chemotherapeutic drug, docetaxel varies significantly between individuals which makes it difficult to determine the best dosage and treatment outcomes for cancer patients. This study explores a data-driven, patient-specific approach to develop a predictive model for personalized therapy. Clinical data from different published studies were gathered and using this data, synthetic dataset was generated to reflect real-world interpatient variability in pharmacokinetic parameters such as, clearance, volume of distribution and area under the curve (AUC), along with other patient factors and clinical characteristics such as, age, gender, weight, cancer type and organ function. After generating synthetic dataset, this dataset was then validated using Python libraries, which enables exploratory data analysis (EDA) and visualization to evaluate biological possibility and clinical significance. Pharmacokinetic patterns were confirmed through correlation analyses, which includes strong inverse relationship between clearance and AUC, and positive correlation between Vd, weight and body surface area. Different predictive models were developed, such as Linear Regression model, Random Forest model, Gradient Boosting model and SVR, to predict AUC from patient-specific variables. Among all of these predictive models, Gradient Boosting model showed the best performance, R² is 0.83, which demonstrates that this model provides feasibility for using synthetic dataset and advanced analytics for the prediction of individualized dosing. This study demonstrates how pharmacokinetic research can benefit from the integration of artificial intelligence and synthetic data to aid in decision making. It strengthens the position of clinical pharmacists in personalized medicine and lays the groundwork for the creation of future instruments that can improve the safety and effectiveness of chemotherapy based on docetaxel.Item Increasing Efficacy of Antibiotic by Combining it with Adjuvants against Resistant Gram-Negative and Gram- Positive Bacteria(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Zueen Shahbaz; CIIT/FA23-RPY-005/LHR; Dr. Ghulam Murtaza; LHR TP 9836Bacterial resistance to currently available treatments is a global problem that raises death rates and treatment costs, emphasizing the need for additional classes of antibacterial drugs or compounds that interact synergistically with antimicrobials. The efficacy of current antibiotics has been considerably compromised by the rapid spread and emergence of antimicrobial resistance in both Gram-positive and Gram-negative pathogens. The objective of current study is to improve antibiotic efficacy by combining ciprofloxacin, amikacin, ceftriaxone, vancomycin and colistin with adjuvants i.e., iron nanoparticles (FeNP), rhamnose, isatin and sinapic acid. Zone of inhibition (ZoI) measurements and minimum inhibitory concentration (MIC) assays were used to evaluate these combinations antibacterial effectiveness against clinically relevant resistant strains of P. aeruginosa and S. aureus. The findings emphasize the potential of adjuvant therapy in overcoming antibiotic resistance. Fe-NP frequently counteracted antibiotic activity, raising MIC values in the majority of combinations despite slight increase in ZoI. Rhamnose exhibited variable outcomes, increasing MIC in some combinations and reducing it in others. Isatin improved antibiotic performance by lowering MIC and increase in ZoI for ciprofloxacin against S. aureus and amikacin and ceftriaxone against P. aeruginosa. Sinapic acid demonstrated selective but potent synergy, especially with ciprofloxacin and amikacin against resistant P. aeruginosa. These results suggest that certain adjuvants, especially isatin and sinapic acid enhanced the activity of antibiotics. Such combinations could enable lower antibiotic doses, reduce side effects and delay the emergence of resistance.Item 1Decoding Survival: Predictive Modeling of Progression-Free Outcomes in Platinum Based Chemotherapy(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Ayesha Yousaf; CIIT/FA23-RPY-001/LHR; Dr. Muhammad Ihtisham Umar; LHR TP 9833The aim of the study is development and evaluation of predictive models for progression free survival in female reproductive tract cancers through clinical and demographic data. Large data of 1595 patients was analyzed having variables like age, patient staus, disease staus, ethnicity, comorbidities, overall survival, response rate, progression free survival, histology. Significant statistical correlation and exploratory data analysis was carried out to figure out corrlation between survival outcomes and patient’s characteristics. Various machine learning algorithms like linear regression, random forest model, support vector machine and XGBoost were used for the prediction of progression free survival. Different performance matrices were used to asses the safety and efficacy of the model. The outcomes show the practicalityand usefullness of predictive machine learning models for the prediction of disease progression, guidance of cutomized treatment strategies and development of clinical decision making in oncology.