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1. A super‐combo‐drug test to detect adverse drug events and drug interactions from electronic health records in the era of polypharmacy

   https://doi.org/10.1002/sim.8490  

   Zhu, Anqi ; Zeng, Donglin ; Shen, Li ; Ning, Xia ; Li, Lang ; Zhang, Pengyue ( February 2020 , Statistics in Medicine)

   Pharmacoinformatics research has experienced a great deal of successes in detecting drug‐induced adverse events (AEs) using large‐scale health record databases. In the era of polypharmacy, pharmacoinformatics faces many new challenges, and two significant challenges are to detect high‐order drug interactions and to handle strongly correlated drugs. In this article, we propose a super‐combo‐drug test (SupCD‐T) to address the aforementioned two challenges. SupCD‐T detects drug interactions by identifying optimal drug combinations with increased AE risks. In addition, SupCD‐T increases the statistical powers to detect single‐drug effects by combining strongly correlated drugs. Although SupCD‐T does not distinguish single‐drug effects from their combination effects, it is noticeably more powerful in selecting an individual drug effect in the multiple regression analysis, where confounding justification between two correlated drugs reduces the power in testing the individual drug effects on AEs. Our simulation studies demonstrate that SupCD‐T has generally better power comparing with the multiple regression analysis. In addition, SupCD‐T is able to select meaningful drug combinations (eg, highly coprescribed drugs). Using electronic health record database, we illustrate the utility of SupCD‐T and discover a number of drug combinations that have increased risk in myopathy. Some novel drug combinations have not yet been investigated and reported in the pharmacology research.

    

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2. City-wide electronic health records reveal gender and age biases in administration of known drug–drug interactions

   https://doi.org/10.1038/s41746-019-0141-x  

   Brattig Correia, Rion ; de Araújo Kohler, Luciana P. ; Mattos, Mauro M. ; Rocha, Luis M. ( December 2019 , npj Digital Medicine)
3. Detecting Drug-Drug Interactions using Protein Sequence-Structure Similarity Networks

   https://doi.org/10.1109/BIBM52615.2021.9669858  

   Islam, Saminur ; Abbasi, Ahmed ; Agarwal, Nitin ; Zheng, Wanhong ; Doretto, Gianfranco ; Adjeroh, Donald A. ( December 2021 , 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM))
4. Minimization of Drug Shortages in Pharmaceutical Supply Chains: A Simulation-Based Analysis of Drug Recall Patterns and Inventory Policies

   https://doi.org/10.1155/2018/6348413  

   Azghandi, Rana ; Griffin, Jacqueline ; Jalali, Mohammad S. ( December 2018 , Complexity)

   The drug shortage crisis in the last decade not only increased health care costs but also jeopardized patients’ health across the United States. Ensuring that any drug is available to patients at health care centers is a problem that official health care administrators and other stakeholders of supply chains continue to face. Furthermore, managing pharmaceutical supply chains is very complex, as inevitable disruptions occur in these supply chains (exogenous factors), which are then followed by decisions members make after such disruptions (internal factors). Disruptions may occur due to increased demand, a product recall, or a manufacturer disruption, among which product recalls—which happens frequently in pharmaceutical supply chains—are least studied. We employ a mathematical simulation model to examine the effects of product recalls considering different disruption profiles, e.g., the propagation in time and space, and the interactions of decision makers on drug shortages to ascertain how these shortages can be mitigated by changing inventory policy decisions. We also measure the effects of different policy approaches on supply chain disruptions, using two performance measures: inventory levels and shortages of products at health care centers. We then analyze the results using an approach similar to data envelopment analysis to characterize the efficient frontier (best inventory policies) for varying cost ratios of the two performance measures as they correspond to the different disruption patterns. This analysis provides insights into the consequences of choosing an inappropriate inventory policy when disruptions take place. 

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5. Drug–Drug Interaction Between Oxycodone and Diazepam by a Combined in Silico Pharmacokinetic and Pharmacodynamic Modeling Approach

   https://doi.org/10.1021/acschemneuro.0c00810  

   Beihong Ji, Ying Xue ( April 2021 , ACS Chemical Neuroscience)

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