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- Hens, Bart, et al.
(author)
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Formulation predictive dissolution (fPD) testing to advance oral drug product development : An introduction to the US FDA funded '21st Century BA/BE' project
- 2018
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In: International Journal of Pharmaceutics. - : Elsevier. - 0378-5173 .- 1873-3476. ; 548:1, s. 120-127
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Research review (peer-reviewed)abstract
- Over the past decade, formulation predictive dissolution (fPD) testing has gained increasing attention. Another mindset is pushed forward where scientists in our field are more confident to explore the in vivo behavior of an oral drug product by performing predictive in vitro dissolution studies. Similarly, there is an increasing interest in the application of modern computational fluid dynamics (CFD) frameworks and high-performance computing platforms to study the local processes underlying absorption within the gastrointestinal (GI) tract. In that way, CFD and computing platforms both can inform future PBPK-based in silico frameworks and determine the GI-motility-driven hydrodynamic impacts that should be incorporated into in vitro dissolution methods for in vivo relevance. Current compendial dissolution methods are not always reliable to predict the in vivo behavior, especially not for biopharmaceutics classification system (BCS) class 2/4 compounds suffering from a low aqueous solubility. Developing a predictive dissolution test will be more reliable, cost-effective and less time-consuming as long as the predictive power of the test is sufficiently strong. There is a need to develop a biorelevant, predictive dissolution method that can be applied by pharmaceutical drug companies to facilitate marketing access for generic and novel drug products. In 2014, Prof. Gordon L. Amidon and his team initiated a far-ranging research program designed to integrate (1) in vivo studies in humans in order to further improve the understanding of the intraluminal processing of oral dosage forms and dissolved drug along the gastrointestinal (GI) tract, (2) advancement of in vitro methodologies that incorporates higher levels of in vivo relevance and (3) computational experiments to study the local processes underlying dissolution, transport and absorption within the intestines performed with a new unique CFD based framework. Of particular importance is revealing the physiological variables determining the variability in in vivo dissolution and GI absorption from person to person in order to address (potential) in vivo BE failures. This paper provides an introduction to this multidisciplinary project, informs the reader about current achievements and outlines future directions.
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| 3. |
- Ribeiro, Antonio, et al.
(author)
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Automatic diagnosis of short-duration 12-lead ECG using a deep convolutional network
- 2018
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In: <em>ML4H: Machine Learning for Health Workshop at NeurIPS</em>, Montréal, Canada, December 2018..
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Conference paper (peer-reviewed)abstract
- We present a model for predicting electrocardiogram (ECG) abnormalities in shortduration 12-lead ECG signals which outperformed medical doctors on the 4th year of their cardiology residency. Such exams can provide a full evaluation of heart activity and have not been studied in previous end-to-end machine learning papers. Using the database of a large telehealth network, we built a novel dataset with more than 2 million ECG tracings, orders of magnitude larger than those used in previous studies. Moreover, our dataset is more realistic, as it consist of 12-lead ECGs recorded during standard in-clinics exams. Using this data, we trained a residual neural network with 9 convolutional layers to map 7 to 10 second ECG signals to 6 classes of ECG abnormalities. Future work should extend these results to cover a large range of ECG abnormalities, which could improve the accessibility of this diagnostic tool and avoid wrong diagnosis from medical doctors.
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