{"id":{"repo_id":"de-montfort","oai_identifier":"oai:dora.dmu.ac.uk:2086/26249"},"canonical_url":"https://search.dev.ndltd.org/etd/de-montfort/oai:dora.dmu.ac.uk:2086/26249","repository":{"repo_id":"de-montfort","name":"De Montfort University","base_url":"https://dora.dmu.ac.uk/server/oai/request"},"display":{"title":"RETROSPECTIVE DESCRIPTIVE STUDY ON POST-MARKET MEDICINE QUALITY-RELATED COMPLAINTS AND THE APPLICATION OF FIELD-BASED DETECTION OF POORQUALITY MEDICINES USING NEAR-INFRARED AND RAMAN SPECTROSCOPIC SCREENING METHODS","abstract":"Substandard and falsified (SF) medicines have remained a critical global health challenge, disproportionately affecting low- and middle-income countries due to weak regulatory systems and limited laboratory infrastructure. This study developed and validated a novel field-deployable framework integrating handheld spectroscopy with chemometric analysis for rapid detection of substandard medicines using Kenya as a case study. The methodology employed a mixed approach, a literature review, retrospective analysis of pharmacovigilance data (2014-2021), and laboratory analysis of field collected samples. A risk-based sampling of high-risk essential tablets, including metronidazole, losartan, and a fixed-dose combination antiretroviral tenofovir/lamivudine/dolutegravir. Laboratory studies assessed the performance of handheld Near-Infrared (NIR) and Raman spectrometers on identical medicine samples. A chemometric workflow incorporating preprocessing and unsupervised methods (Principal Component Analysis (PCA), K-means clustering, and Hierarchical Clustering Analysis (HCA)) was developed for spectral anomaly detection with findings validated using High-Performance Liquid Chromatography (HPLC). The main findings demonstrate that NIR enabled rapid, non-destructive screening but showed limitations in sensitivity, particularly for high-dose formulations, resulting in false positives. Raman spectroscopy provided superior molecular specificity, accurately detecting subtle anomalies. For metronidazole, samples flagged by both techniques were not confirmed as substandard by HPLC. For losartan, PCA-based outlier detection showed varying sensitivity. Of the samples that exceeded the T² threshold, 2.00% (n=1) flagged by NIR and 6.00% (n=3) flagged by Raman were confirmed as substandard by HPLC. In contrast, of those that exceeded the Q threshold, only 2.00% (n=1) were confirmed as substandard by HPLC for each method. Results from unsupervised clustering showed that K-means was more sensitive than HCA, as it identified more spectral outliers for losartan samples, 52.00% (n=26) by Raman compared to 38.00% (n=19) by NIR. HCA flagged an identical outlier rate for both techniques (44.00%, n=22), with 4.00% (n=2) confirmed as substandard by HPLC across all methods. A larger subset failed manufacturer release limits but remained within regulatory limits: 20.00% (n=10) by NIR and 26.00% (n=13) by Raman. This study establishes a tiered post-market surveillance framework using handheld NIR for rapid screening, Raman for analysis of flagged samples, and confirmation with HPLC. A novel multivariate equivalence framework using clustering metrics assesses spectral proximity of field samples to authentic reference tablets, serving as a pharmaceutical quality surrogate. This proactive approach, enables real-time decision-making at the point of sampling. The thesis provides significant new knowledge on portable spectroscopy for SF medicine detection within operational supply chains, demonstrating its feasibility, strengths, and limitations.","abstract_html":"Substandard and falsified (SF) medicines have remained a critical global health challenge, disproportionately affecting low- and middle-income countries due to weak regulatory systems and limited laboratory infrastructure. This study developed and validated a novel field-deployable framework integrating handheld spectroscopy with chemometric analysis for rapid detection of substandard medicines using Kenya as a case study. The methodology employed a mixed approach, a literature review, retrospective analysis of pharmacovigilance data (2014-2021), and laboratory analysis of field collected samples. A risk-based sampling of high-risk essential tablets, including metronidazole, losartan, and a fixed-dose combination antiretroviral tenofovir/lamivudine/dolutegravir. Laboratory studies assessed the performance of handheld Near-Infrared (NIR) and Raman spectrometers on identical medicine samples. A chemometric workflow incorporating preprocessing and unsupervised methods (Principal Component Analysis (PCA), K-means clustering, and Hierarchical Clustering Analysis (HCA)) was developed for spectral anomaly detection with findings validated using High-Performance Liquid Chromatography (HPLC). The main findings demonstrate that NIR enabled rapid, non-destructive screening but showed limitations in sensitivity, particularly for high-dose formulations, resulting in false positives. Raman spectroscopy provided superior molecular specificity, accurately detecting subtle anomalies. For metronidazole, samples flagged by both techniques were not confirmed as substandard by HPLC. For losartan, PCA-based outlier detection showed varying sensitivity. Of the samples that exceeded the T² threshold, 2.00% (n=1) flagged by NIR and 6.00% (n=3) flagged by Raman were confirmed as substandard by HPLC. In contrast, of those that exceeded the Q threshold, only 2.00% (n=1) were confirmed as substandard by HPLC for each method. Results from unsupervised clustering showed that K-means was more sensitive than HCA, as it identified more spectral outliers for losartan samples, 52.00% (n=26) by Raman compared to 38.00% (n=19) by NIR. HCA flagged an identical outlier rate for both techniques (44.00%, n=22), with 4.00% (n=2) confirmed as substandard by HPLC across all methods. A larger subset failed manufacturer release limits but remained within regulatory limits: 20.00% (n=10) by NIR and 26.00% (n=13) by Raman. This study establishes a tiered post-market surveillance framework using handheld NIR for rapid screening, Raman for analysis of flagged samples, and confirmation with HPLC. A novel multivariate equivalence framework using clustering metrics assesses spectral proximity of field samples to authentic reference tablets, serving as a pharmaceutical quality surrogate. This proactive approach, enables real-time decision-making at the point of sampling. The thesis provides significant new knowledge on portable spectroscopy for SF medicine detection within operational supply chains, demonstrating its feasibility, strengths, and limitations.","abstract_has_math":false,"creators":["Toroitich, Anthony Martin"],"institution":"De Montfort University","degree_name":"PhD","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-07","date_published":"2025-07","updated_at":"2026-07-24T06:18:33Z","subjects":[],"languages":[],"rights":[],"rights_urls":["https://dora.dmu.ac.uk/bitstreams/4563ae2a-9572-431a-a9d8-54b02ad190a7/download"],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Toroitich, Anthony Martin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2025-07"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Faculty of Health and Life Sciences"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["De Montfort University"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://hdl.handle.net/2086/26249"]},{"key":"dc:type","label":"Dc Type","values":["Thesis or dissertation"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["PhD"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["https://dora.dmu.ac.uk/bitstreams/4563ae2a-9572-431a-a9d8-54b02ad190a7/download"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://dora.dmu.ac.uk/bitstreams/9c22a1e4-eb46-4790-8c98-47d7b0e63ed8/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Substandard and falsified (SF) medicines have remained a critical global health challenge, disproportionately affecting low- and middle-income countries due to weak regulatory systems and limited laboratory infrastructure. 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The main findings demonstrate that NIR enabled rapid, non-destructive screening but showed limitations in sensitivity, particularly for high-dose formulations, resulting in false positives. Raman spectroscopy provided superior molecular specificity, accurately detecting subtle anomalies. For metronidazole, samples flagged by both techniques were not confirmed as substandard by HPLC. For losartan, PCA-based outlier detection showed varying sensitivity. Of the samples that exceeded the T² threshold, 2.00% (n=1) flagged by NIR and 6.00% (n=3) flagged by Raman were confirmed as substandard by HPLC. In contrast, of those that exceeded the Q threshold, only 2.00% (n=1) were confirmed as substandard by HPLC for each method. Results from unsupervised clustering showed that K-means was more sensitive than HCA, as it identified more spectral outliers for losartan samples, 52.00% (n=26) by Raman compared to 38.00% (n=19) by NIR. 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