Data Analytics Helps Predicting Drug Efficacy

The change in the schedule and the dosage of drug while administrating treatment for a disease like cancer can have a significant effect on the drug efficacy. Doctors / physicians who are treating patients and pharmaceutical companies who are manufacturing those drugs understand the importance of this process. Frequently, depending on how the patient is responding to a drug, the medication needs to be increased or decreased and the quantity of the dosage altered. Do you think data () Bi & analytics can help here? Does this sound like being very subjective to a patient and the response of the metabolic reaction of the patient? Does this not sound like a hit and trial mechanism approach to solving such a serious problem like cancer and calls for a more structured approach possibly using data analytics ? What if a fixed schedule and dosage amount can be reached which will bring about the right change in cancer cells in a patient? How would it be for the pharmaceutical companies to use the data and be able to predict miraculous results?

Predictive Data Analytics:

All this may not have been possible earlier, but with the emergence of predictive analytics models being built for the pharmaceutical drug manufacturing process, this is possible now. There is extensive Pharma Data Analytics being done to produce predictive models which have been developed and are being used by the pharmaceutical companies to help simulate and predict possible outcomes of different schedules and dosage levels. Pharmacokinetic, also referred as PK in the pharmaceutical world, models help in predicting the dosage of a drug by understanding its uptake in the patient. This has to be followed by complex analysis of the reaction it will produce while interacting with the different proteins, enzymes and various chemical reactions en-route its absorption and assimilation within the body. There can be multiple combinations of the different chemical reactions which can happen in the body and these are being predicted by Pharmacodynamic, also referred as PD in the pharmaceutical world, mathematical models cell and to be able to work at the tumor level.

The pharmaceutical industry is at the genesis of being able to use such data analytics and implement it to produce successful results. There are a lot of factors to be considered while building and running these mathematical models, including the chemical composition of the tumor, level of spread of the drug at the specified spot, the reaction of the chemical composition of the drug and the tumor etc.

At () Incedo Inc., we believe that as important as it is to understand the significance of these models, it is equally vital to visualize that the models are as good as the data on which they are being run. Data for data analytics is like oxygen for life. No data analytics can give you any result and the number of false positives will depend on the quality of data being supplied for performing those analytics. Pharmaceutical companies across the world are collecting data from hospitals, medical prescriptions, patient diagnosis, patient treatment history, patients’ medical records, drug prescriptions, insurance company claims, drug distribution counters etc. and trying to work out the best models. This is also coupled with the use of the human gene program which is also being fed into these to test the right predictive analytics for the pharmaceutical companies. Another challenge associated with this while building the data analytic models for the pharmaceutical companies is the amount of variable parameters for which we have limited information (or say less data as what would have been expected).

The idea of using models to predict outcomes is all about being able to become more efficient and cost effective. Incedo Inc., with its team of domain experts in the Life Sciences space, has been consistently partnering with clients globally to build robust and scalable Data Analytics solutions and services.

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