Data Science in Clinical Pharmacology and Drug Development for Improving Health Outcomes in Patients

Peck, Richard; Shah, Pratik; Vamvakas, Spiros; Graaf, Piet H. van der

doi:10.1002/cpt.1803

Cited by 6 publications

(8 citation statements)

References 28 publications

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“…17,21 It provides information on all patients that actually use the drug and not just a subgroup of them, as often in clinical trials, thereby leading to more generalizable results and optimized patient outcomes. 22 It is important to highlight that it is not immediately possible to set up an accuracy threshold to be reached, particularly such that holistically applies in all research scenarios. A high level of accuracy may be achieved in a specific research context whereas in others this may not be possible.…”

Section: Discussionmentioning

confidence: 99%

Artificial Neural Network vs. Pharmacometric Model for Population Prediction of Plasma Concentration in Real‐World Data: A Case Study on Valproic Acid

Soeorg

Sverrisdóttir

Andersen

et al. 2022

Clin Pharma and Therapeutics

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We compared the predictive performance of an artificial neural network to traditional pharmacometric modeling for population prediction of plasma concentrations of valproate in real‐world data. We included individuals aged 65 years or older with epilepsy who redeemed their first prescription of valproate after the diagnosis of epilepsy and had at least one valproate plasma concentration measured. A long short‐term memory neural network (LSTM) was developed using the training data set to fit the LSTM and the test data set to validate the model. Predictions from the LSTM were compared with those obtained from the population predictions from a pharmacometric model by Birnbaum et al. which had the best predictive performance for population predictions of valproate concentrations in Danish databases. We used the cutoff of ± 20 mg/L of prediction error to define good predictions. A total of 1,252 individuals were included in the study. The LSTM fitted using the training data set had poor predictive performance in the test data set, but better than that of the pharmacometric model. The proportion of individuals with at least one predicted concentration within ± 20 mg/L of observed concentration was largest in case of the LSTM (64.4%, 95% confidence interval (CI): 58.4–70.2%) compared with the pharmacometric model by Birnbaum et al. (49.8%, 95% CI: 47.0–52.6%). LSTM shows better predictive performance to predict valproate plasma concentrations compared with a traditional pharmacometric model in the investigated setting with real‐world data in older patients with epilepsy where information on exact timepoints for both dosing and plasma concentration measurement are missing.

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Section: Discussionmentioning

confidence: 99%

Artificial Neural Network vs. Pharmacometric Model for Population Prediction of Plasma Concentration in Real‐World Data: A Case Study on Valproic Acid

Soeorg

Sverrisdóttir

Andersen

et al. 2022

Clin Pharma and Therapeutics

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“…It was therefore not a coincidence that the second themed issue of this year was dedicated to "Data Science," which brought together clinical pharmacology with science and methodology from areas like artificial intelligence, machine learning, real-world data, and large-scale multi-omics. 2 This was also the first example of an issue fully dedicated to a particular theme, including all original research articles. It also introduced the first CPT Tutorials, 3,4 a new article type for the journal providing practical educational material on tools, methodologies, and approaches in clinical pharmacology [5][6][7] ( Table 1).…”

Section: Editorialmentioning

confidence: 96%

“…In addition to the disciplines where CPT has traditionally been the leading publication in the field, we identified new and emerging areas for the journal to focus on, such as precision medicine, data science, global heath and special populations, and novel modalities and therapeutic approaches. It was therefore not a coincidence that the second themed issue of this year was dedicated to “ Data Science ,” which brought together clinical pharmacology with science and methodology from areas like artificial intelligence, machine learning, real‐world data, and large‐scale multi‐omics 2 . This was also the first example of an issue fully dedicated to a particular theme, including all original research articles.…”

Section: Figurementioning

confidence: 99%

Clinical Pharmacology and Therapeutics: 2020 in Review

Graaf

2020

Clin Pharma and Therapeutics

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“…Furthermore, contributions for themed issues are no longer by invitation only and open calls for papers are now being issued. Six themed issues have been published using this new format in the last 2 years, 3–8 and 3 are scheduled for publication in 2022 ( Table 1 ). Submissions for the “Transporter” theme (scheduled for publication in September 2022) are still being accepted.…”

Section: Figurementioning

confidence: 99%