Common Use Cases
The pharmaceutical industry poses unique challenges to companies which seek to extract profits from chemical or biological compounds due to the extremely high investment cost of drug development, validation, marketing, and adoption. With unique datasets available from chemical, manufacturing, patient, clinical trial, and RWD, data scientists are in a position to help streamline operations along the entire spectrum of the industry. SFL Scientific has expert domain knowledge and an intimate understanding of personalized medicine, -omics, drug and clinical data, deploying neural networks and novel solutions to R&D type problems.
We develop, implement, and validate solutions using machine learning and deep neural networks to pharmaceutical and biotechnology companies and academic organizations worldwide.
Model biological systems to predict outcomes and help identify new potential-candidate molecules with a high probability of successful drug development.
Deep Learning: Scrape and model existing and novel datasets for physicochemical properties, formulation or target prediction, and absorption, distribution, metabolism, excretion, and Tox.
Risk: Predictive modeling of risk factors associated with drug reaction and drug-to-drug interactions.
Patient Selection: Data-based patient selection can create more targeted pools of candidates for clinical trials ensuring the best population for each drug is selected. Target and segment specific populations and candidates, enabling more powerful and less expensive trials.
Real-Time & Mobile Solutions: Real-time patient monitoring can detect anomalies from trials to immediately identify safety risks. Virtual patient coaching for individuals can be used to improve patient outcomes.
Medication Adherence: Engage and guide trial patients with reminders to maintain dosing schedule and compliance with trial parameters to obtain better trial data. Model and use predictive analytics for patient adherence.
Risk: Predictive analytics to reduce the risk of drug-drug interactions and adverse patient effects.
Real-Time and Preventative: Track and guide patient dosing behavior to reduce risk when patients do not follow their prescriptions. Model what drives patients to stop taking medications and which patients are likely to 'drop-out'.
Deep Learning & NLP: Scrape literature, review EHRs, and create powerful models for drug repurposing. Use computational tools to sift through sequencing and genomic data to discover novel drug-disease pairs and validate CRO trials.
Scrape claims data or electronic medical records into more efficient drug development. Increase personalization of therapy and treatment to improve patient relationships. Identify new markets and underserved populations.
Providers & Payors: Aggregate and maintain data stores, combining predictive analytics to analyze patient outcomes, studies, claims, and other real-world data, to investigate beyond clinical trials.
Sentiment Analysis & Targetting: Leverage unstructured data physician information, EMRs/EHRs, media, and payer information to flag potential safety issues and explore new markets. Establish predictive methods ahead of drug releases.