Use of Exposure Data to Establish Causality in Drug–Adverse Event Relationships: An Example with Desvenlafaxine
Causality algorithms help establish relationships between drug use and adverse event (AE) occurrence. High drug exposure leads to a higher likelihood of an AE being classified as an adverse drug reaction (ADR). However, there is a knowledge gap regarding what concentrations are predictive of ADRs, a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Pharmaceuticals |
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| Online Access: | https://www.mdpi.com/1424-8247/17/1/69 |
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| author | Andrea Rodríguez-Lopez Gina Mejía-Abril Pablo Zubiaur Sofía Calleja Manuel Román Francisco Abad-Santos Dolores Ochoa |
| author_facet | Andrea Rodríguez-Lopez Gina Mejía-Abril Pablo Zubiaur Sofía Calleja Manuel Román Francisco Abad-Santos Dolores Ochoa |
| author_sort | Andrea Rodríguez-Lopez |
| collection | DOAJ |
| description | Causality algorithms help establish relationships between drug use and adverse event (AE) occurrence. High drug exposure leads to a higher likelihood of an AE being classified as an adverse drug reaction (ADR). However, there is a knowledge gap regarding what concentrations are predictive of ADRs, as this has not been systematically studied. In this work, the Spanish Pharmacovigilance System (SEFV) algorithm was used to define the relationship between the AE occurrence and drug administration in 178 healthy volunteers participating in five desvenlafaxine single-dose clinical trials, a selective serotonin and norepinephrine reuptake inhibitor that may cause dizziness, headache, nausea, dry mouth, constipation and hyperhidrosis. Eighty-three subjects presented 172 AEs that were classified as possible (101), conditional (31), unrelated (24) and probable (16). AUC<sub>∞</sub> and C<sub>max</sub> were significantly higher in volunteers with vs. without ADRs (5981.24 ng·h/mL and 239.06 ng/mL and 4770.84 ng·h/mL and 200.69 ng/mL, respectively). Six of 19 subjects with conditional AEs with an SEFV score of 3 points presented an AUC<sub>∞</sub> ≥ 6500 ng·h/mL or a C<sub>max</sub> ≥ 300 ng/mL (i.e., above percentile 75) and were summed one point on their SEFV score and classified as “possible” (4 points), improving the capacity of ADR detection. |
| first_indexed | 2024-03-08T10:38:52Z |
| format | Article |
| id | doaj.art-da1aa8d34b1e4abb8ea66f861b456e90 |
| institution | Directory Open Access Journal |
| issn | 1424-8247 |
| language | English |
| last_indexed | 2024-03-08T10:38:52Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceuticals |
| spelling | doaj.art-da1aa8d34b1e4abb8ea66f861b456e902024-01-26T18:05:40ZengMDPI AGPharmaceuticals1424-82472024-01-011716910.3390/ph17010069Use of Exposure Data to Establish Causality in Drug–Adverse Event Relationships: An Example with DesvenlafaxineAndrea Rodríguez-Lopez0Gina Mejía-Abril1Pablo Zubiaur2Sofía Calleja3Manuel Román4Francisco Abad-Santos5Dolores Ochoa6Clinical Pharmacology Department, Hospital Universitario de La Princesa, Faculty of Medicine, Instituto de Investigación Sanitaria La Princesa (IP), Universidad Autónoma de Madrid (UAM), 28006 Madrid, SpainClinical Pharmacology Department, Hospital Universitario de La Princesa, Faculty of Medicine, Instituto de Investigación Sanitaria La Princesa (IP), Universidad Autónoma de Madrid (UAM), 28006 Madrid, SpainClinical Pharmacology Department, Hospital Universitario de La Princesa, Faculty of Medicine, Instituto de Investigación Sanitaria La Princesa (IP), Universidad Autónoma de Madrid (UAM), 28006 Madrid, SpainClinical Pharmacology Department, Hospital Universitario de La Princesa, Faculty of Medicine, Instituto de Investigación Sanitaria La Princesa (IP), Universidad Autónoma de Madrid (UAM), 28006 Madrid, SpainClinical Pharmacology Department, Hospital Universitario de La Princesa, Faculty of Medicine, Instituto de Investigación Sanitaria La Princesa (IP), Universidad Autónoma de Madrid (UAM), 28006 Madrid, SpainClinical Pharmacology Department, Hospital Universitario de La Princesa, Faculty of Medicine, Instituto de Investigación Sanitaria La Princesa (IP), Universidad Autónoma de Madrid (UAM), 28006 Madrid, SpainClinical Pharmacology Department, Hospital Universitario de La Princesa, Faculty of Medicine, Instituto de Investigación Sanitaria La Princesa (IP), Universidad Autónoma de Madrid (UAM), 28006 Madrid, SpainCausality algorithms help establish relationships between drug use and adverse event (AE) occurrence. High drug exposure leads to a higher likelihood of an AE being classified as an adverse drug reaction (ADR). However, there is a knowledge gap regarding what concentrations are predictive of ADRs, as this has not been systematically studied. In this work, the Spanish Pharmacovigilance System (SEFV) algorithm was used to define the relationship between the AE occurrence and drug administration in 178 healthy volunteers participating in five desvenlafaxine single-dose clinical trials, a selective serotonin and norepinephrine reuptake inhibitor that may cause dizziness, headache, nausea, dry mouth, constipation and hyperhidrosis. Eighty-three subjects presented 172 AEs that were classified as possible (101), conditional (31), unrelated (24) and probable (16). AUC<sub>∞</sub> and C<sub>max</sub> were significantly higher in volunteers with vs. without ADRs (5981.24 ng·h/mL and 239.06 ng/mL and 4770.84 ng·h/mL and 200.69 ng/mL, respectively). Six of 19 subjects with conditional AEs with an SEFV score of 3 points presented an AUC<sub>∞</sub> ≥ 6500 ng·h/mL or a C<sub>max</sub> ≥ 300 ng/mL (i.e., above percentile 75) and were summed one point on their SEFV score and classified as “possible” (4 points), improving the capacity of ADR detection.https://www.mdpi.com/1424-8247/17/1/69adverse eventadverse drug reactionscausality algorithmssafetypharmacokinetics |
| spellingShingle | Andrea Rodríguez-Lopez Gina Mejía-Abril Pablo Zubiaur Sofía Calleja Manuel Román Francisco Abad-Santos Dolores Ochoa Use of Exposure Data to Establish Causality in Drug–Adverse Event Relationships: An Example with Desvenlafaxine Pharmaceuticals adverse event adverse drug reactions causality algorithms safety pharmacokinetics |
| title | Use of Exposure Data to Establish Causality in Drug–Adverse Event Relationships: An Example with Desvenlafaxine |
| title_full | Use of Exposure Data to Establish Causality in Drug–Adverse Event Relationships: An Example with Desvenlafaxine |
| title_fullStr | Use of Exposure Data to Establish Causality in Drug–Adverse Event Relationships: An Example with Desvenlafaxine |
| title_full_unstemmed | Use of Exposure Data to Establish Causality in Drug–Adverse Event Relationships: An Example with Desvenlafaxine |
| title_short | Use of Exposure Data to Establish Causality in Drug–Adverse Event Relationships: An Example with Desvenlafaxine |
| title_sort | use of exposure data to establish causality in drug adverse event relationships an example with desvenlafaxine |
| topic | adverse event adverse drug reactions causality algorithms safety pharmacokinetics |
| url | https://www.mdpi.com/1424-8247/17/1/69 |
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