SAFE_MEDICATION - A STUDY OF USING ARTIFICIAL INTELLIGENCE TO RECOGNISE MEDICATION ERRORS
Medication errors in patients are a global problem. They can negatively affect patients and be costly for hospitals and medical clinics. In 2021, a 28-year-old man with heart problems was admitted to a hospital in Porto Alegre. Due to a pharmacy error and insufficient monitoring in the administration, he received a dose 10 times higher than prescribed. This caused serious and probably irreversible damage to the patient. Reading the news and following the case in the media has encouraged research in scientific databases, searching for information and data on medication errors, as well as emerging technologies to reduce the occurrence of adverse medication events. Based on the findings of an English study that proved that errors occur at the drug prescription stage, the first stage of this research focused on drug dosage errors. The aim of this study is to develop an application based on artificial intelligence that can recognise these errors and help prevent them. The application uses a neural network to analyse prescriptions and warn of possible cases of incorrect dosage. The computer program was developed using a neural network and the drug dosage error recognition system using Python and Keras. The system was trained with 10 drugs and correct and incorrect dosage cases. A graphical interface was created to input and display new case data. Neural networks with different configurations were tested to obtain high accuracy with the training and validation data. A confusion matrix was used to assess the accuracy of the network for cases not used for training. The accuracy was approximately 96%, but problems were found in certain intervals. The errors are due to the need for more training, higher processing capacity and a cloud server. The results of the first stage of the research indicate the feasibility of using a neural network to recognise medication dosage errors and thus preventing the associated risks. Such a method could prevent cases like the one in Porto Alegre. Future studies could incorporate more types of drugs, allergies, drug interactions, pre-existing illnesses and other relevant factors into the system.
In silico Screening of Forty Antiviral Phytochemicals as Inhibitors to the Envelope Protein of Dengue Virus Serotype 2 (DENV-2)
Infections by the Dengue virus (DENV) cause a disease amonghumansreferred to as Dengue fever, which causes thousands of fatalities globally. There is no existing treatment as of yet that successfully targets DENV. Among the factors thatdeterminetheentry of the virus and severity of the disease is the envelope(E) protein of DENV. This study aimed to examine forty antiviral phytochemicals enumeratedinpaststudiesaspossibleinhibitorstotheEprotein of DENV to provide candidates to aid in drug discovery against DENV. The phytochemicals were screened for their likelihood of inhibition of the E protein using AutoDock Suite and LigPlot+. Seven phytochemicals produced favorable binding affinities to the E protein, which are based on the interactions between the phytochemicals and amino acidsintheactivesiteoftheEprotein.Lipinski’s rule of 5 was then used to screen the seven phytochemicals for oral bioavailability. Glabridin has a binding affinity of -7.6 kcal/mol and was predicted to be orally bioavailable. This phytochemical interacts with amino acids in the E protein active site through hydrogen bonds to Asn355, andPhe337, as well as ten hydrophobic interactions. These interactions ensure that glabridin is able to specifically target and fit intotheactivesiteoftheEprotein, preventing its binding to the host cell and activating its viral proliferation. Glabridin is known to be found in the roots of licorice plants, providing anatural source for a possible cure for Dengue fever.