Kavya

Machine learning is a branch of artificial intelligence that deals with and focuses on algorithms, improving their accuracy through the collection of data, resembling human intelligence. Machine learning has been developed since its inception. It has become a vital resource in human resources as well. Nowadays, machine learning is not only used in technical and engineering fields but also in the medical field. It is employed in healthcare, treatment, drug discovery, and drug development, among other applications. Pharmacotherapeutics pertains to the use of drugs for prevention, treatment, diagnosis, and modification of normal functions. Machine learning has become imperative in the medical field, healthcare, drug discovery, and development. It is used in the development of a drug by creating a lead molecule and determining its effects on the body through technical methods. Machine learning is utilized in diagnostics, such as during EEG, ECG, MRI, CT scans, and many other diagnostic procedures. It is employed in clinical pharmacology where humans are used to measure drug effects. In academic practices for pharmacology subjects, software is used to calculate doses and conduct experiments technically, as it is prohibited to harm animals under PCI guidelines. Machine learning in pharmacotherapeutics plays a significant role in the medical field, aiding in drug discovery, drug development, diagnosis, and disease treatment. It is used in neural networks of artificial intelligence, where input and output act as neurons, contributing to the treatment of various diseases and disorders. In this manner, machine learning holds a distinct and vital role in pharmacotherapeutics.  

The term biosimilar is used for a subsequently launched version of a biologic product which is similar in terms of quality, safety, and efficacy to an already licensed “Reference Biologic” product. The primary purpose of biosimilars is to reduce the healthcare costs associated with the use of biologics and thereby increase access to healthcare. Unlike small molecule generics, the bioequivalence approach is not considered appropriate for the approval of biosimilars. The approval of biosimilars is based on a stepwise comparability exercise with the “Reference Biologic”, starting with a comprehensive physicochemical and biological characterization. The extent and nature of the required nonclinical in vivo studies and clinical studies depend on the level of evidence obtained from the previous steps. Regulations require that the “Reference Biologic” should have been licensed/approved in the same country/region or in other ICH countries based on a full registration dossier. Apart from the comparability exercise, regulations also deal with indication extrapolation, pharmacovigilance, and substitution and interchangeability. This chapter also briefly describes the considerations for exclusivity, market access, and commercialization of biosimilars.

Mosquitoes are a major vector for diseases such as malaria, dengue, and yellow fever. Mosquito repellents today contain many synthetic chemicals, such as DEET, picardin, and permethrin. To safely repel mosquitoes, less effective alternatives made from essential oils have been used. To try to increase the effectiveness of the essential oil repellents, tests were conducted to find out if the essential oils have increased effectiveness when used in conjunction to each other. It was hypothesized that the essential oil mixture containing lemon eucalyptus and rosemary would make the most effective mosquito repellent. To test the hypothesis, a custom-made mosquito testing chamber with an aquarium warmer was used. A sugar based attractor and the prospective repellent solution were placed on a piece of cloth, which was tied to the aquarium warmer. The aquarium warmer was then placed into the testing chamber, where the mosquitoes resided. The effectiveness of the mosquito repellent was determined by the number of landings on the aquarium warmer over time. The results showed that the mosquito repellent made from Bael and eucalyptus was the most effective mosquito repellent.

Pharmaceutical R&D is failing with an era of triple obstacles – increased timelines for approval, high cost and even higher failure rates. The world around us is constantly putting pressure on creating completely new solutions for about 4000 diseases with known molecular basics and many other diseases without basic intellect. There comes a need to remove the wide gap in innovation showcasing a challenge for drug discovery. The shockingly tedious drug development process is forcing us to find more effective solutions and drug repurposing can be a starting point where the clear understanding of the de novo pathway for an existing drug essentially unlocks the full potential for repurposing a drug for other diseases. Drug repositioning (also known as drug repurposing, re-profiling, re-tasking or therapeutic switching) is the application of known  drugs and compounds to new indications (i.e., new diseases). This idea dates back with cases like Viagra, Thalidomide, and Aspirin etc. There are a number of approaches to drug repurposing, but these are ultimately an expedition into new area. Scientists have to justify examining a compound in a different disease state, so they often make a hypothesis based on possible associations between mechanisms. A significant advantage of drug repositioning over traditional drug development is that since the repositioned drug has already passed a significant number of toxicity and other tests, its safety is known and the risk of failure for reasons of adverse toxicology are reduced. The possibilities are endless, and animal models are elevating drug repurposing's potential.

To study the effect of co morbidities, quality of life, motor and cognitive dysfunction and assess the influence of age, gender in stroke patients. Observe prescribing pattern of drugs. Patients were evaluated for following parameters Age, Gender, Motor FIM, Cognitive FIM, QOL, Comorbidities. Comorbid conditions were assessed using CCI. Patient’s disability was assessed using FIM. The ADL was recorded according to Katz Index. Different Prescription patterns were identified. The incidence of stroke is maximum in the age group of 51-60 years which comprises 30% patients. Stroke is more common in males than females. Smoking and Alcoholism were detected as most common risk factors. FIM Scores of physical and cognitive disability are useful to measure patient’s progress and showed improvement or functional gain in the patients. Hypertension and Diabetes Mellitus were most frequent comorbidities in Stroke. Various classes of drugs were administered to patients. Calcium channel blockers, Beta Blockers, Diuretics, ACE Inhibitors, ARB’s and combinations. Phenytoin, Piracetam, Mannitol, Atorvastatin, Aspirin, Enoxaparin were prescribed. There should be a standard stroke pharmacotherapy for better usage and patient compliance. The incidence of Stroke has significant impact in the quality of Life of the patients. Various risk factors associated with Stroke and presence of comorbidities deteriorate the patient’s progress. FIM scores clinically assess physical and cognitive disability. The standard prescribing patterns should be followed for pharmacotherapy to improve patient care.