The pharmaceutical industry has always deployed technology to drive innovation and advancements in order to better serve patients. As technology is growing rapidly, pharma can use it to its advantage in places like transforming research or drug innovation. Here are five technologies that can change the game in pharma.
Virtual Reality
The pharmaceutical industry can use VR to process and visualize data for research. Pharma companies already use virtual reality solutions for drug research. Scientists depend on virtual reality to ascertain the molecule that can have an impact on the disease target. VR helps improve the effectiveness of pre-developed phases of drugs and helps researchers observe the interaction of molecules to re-engineer them for medical use. VR can also be used to train specialists to handle complex equipment with precision through practice sessions. This will increase the quality of training and reduce operational errors. VR can be used for diagnosing illnesses and eliminate the need for invasive procedures. VR solutions can be used to treat patients suffering from anxiety disorders, phobias or panic disorders by helping the patient build coping strategies. Rehabilitation sessions powered by VR technologies combined with traditional therapy can help patients recover faster.
Big Data
Big Data and Analytics can help pharmaceutical industry in increasing efficiency in research and development, optimize innovation and create useful tools for the healthcare industry. Big Data is also being used to develop safer and more effective drugs and as a form of predictive modelling, through clinical and molecular data. It is also being utilized in clinical trials and the selection of patients based on genetic make-up, specific populations, persons which means faster trials with smaller sample sizes and better success rates. Big data is also being used to monitor trials in real time to reduce delays, enable safety measures quickly and lower costs. This can help with better success rates.
3D Printing
3D printing can enable pharmaceutical industry to produce customized solutions for patients. Drug manufacturing through 3D printing has enabled patients to receive customized medicine as it offers flexibility in formulation. It has also helped in precision in dosage that isn’t possible through conventional drug production. More research on how to use 3D printing in drug manufacturing can ensure that technology is utilized to the fullest extent. The benefits include lower costs, reduced time, decreased intra-operative operations and more safety in organ related surgeries. 3D printing of organs for risky procedures can ease and lower operation time and also increase success in surgeries. Replication of tissues and cells can be used in research to produce customized products for patient-centric treatment.
Drug Adherence Technology
Many healthcare professionals and pharma researchers face the challenge of making sure their patients take the medication in the right dosage and on time. Low drug adherence can cost researchers and skew clinical trials, and patients suffer as it may lead to poorer health. Digital pill systems that track the drug adherence of patients are the newest technology in pharma. These systems comprise of ingestible sensors, wearable sensor patches and a mobile app. Once the patient takes the pill, the sensor on the pill communicates with a patch attached to the patient’s skin. The sensor also tracks the health patterns of patients and the effectiveness of medications. The information is used by researchers or doctors to develop better treatment options for the patients. Digital personal assistants can also help patients take their prescribed medication regularly and speed up the recovery process.
Artificial Intelligence (AI)
AI can perform tasks that usually require human intelligence and is of three types, machine learning, algorithms and deep learning. As of now, AI solutions in healthcare are based on human-created algorithms. For instance, the algorithms can recommend the best drugs using the patient’s medical history and data. Machine learning relies on neural networks as they are modelled after human brains. Some applications include the use of robots, smart devices and virtual assistance in research and treatment. It is also used for identifying diseases, diagnosing illnesses, radiology and radiotherapy planning, personalized medicine and drug discovery. Deep learning, also based on artificial neural networks can be applied in diagnostics to analyze images through size, colour and shape. Companies that adopt AI will gain a strategic advantage as its implementation can improve the success rate of drug development.
