Precision Medicine: Speeding The Go-To-Market Strategy

The global precision medicine market is estimated to reach $216.75B by 2028. Key market players, like Johnson & Johnson, IBM, Intel Corporation, Novartis, and many others, have strategic initiatives that can force industry market growth. There are also many startups, bringing high value and influence on the global healthcare market.

Outside of the large companies, there are numerous advancements in the life science industry. These have paved the way for precision medicine solutions development, which is still one of the most progressive trends within healthcare today. And, unlimited levels of progress are projected in the upcoming years.

Better Care from Personal Treatments

Precision medicine is an approach to patient care that allows a doctor to select individualized treatment plans, based on a genetic understanding of their patient’s specific make-up and disease process. Such plans will often consist of specific drugs, created for a particular patient. In this respect, the development of unique drug compounds with fine-tuned properties is one of the central pieces in this puzzle, although it involves a complex and time-consuming R&D process.

How complex is the process and can the go-to-market strategy be sped up?

A typical drug design lifecycle inevitably goes through several stages:

• Discovery and research
• Clinical trials
• Development
• Regulatory approval
• Production and marketing

The full process usually lasts about 10 to 15 years. Typically, only one drug out of thousands of candidates reach the patient.

New Drug Creation

The cost of a conventional drug, from initial idea to final approval and finally to commercial distribution, is about $2.6B . Agility and precision are crucial factors in drug development, where about 14% of candidates emerge from the exhaustive testing process to gain FDA approval.

The design of new materials is often a resource- and time-consuming endeavor. Therefore, material science has discovered only a tiny piece of an astronomical set of potential compounds. Currently, beliefs state that approximately 10⁸ drug-like molecules have been synthesized thus far, out of more than 10⁶⁰ possible ones.

Judging a potential drug candidate involves examining billions of data points, which would be impossible by humans alone. Advanced machine learning techniques are needed to come up with fresh insights by generalizing vast amounts of data, finding hidden relations, and generating new subtle solutions, all who have been previously overlooked by traditional approaches.

Using Artificial Neural Networks for Discovery

A recent review has proven that that artificial neural networks are ideal for unearthing these possibilities. Such systems could help in the search for new candidate compounds, speed up complex computer simulations, propose various routes of synthesis for a particular drug, and help assess large amounts of clinical trial data.

Personalized medicine is on the front lines now. We are moving towards a data-driven, individualized, patient-centric system, where genetic makeup is used to guide diagnoses and treatment.

And this is all just the tip of the iceberg; a tiny peek at what lies ahead for the potential of personalized, precision medicine. As healthcare becomes ever more personal to the patient, future precision treatments will pave the landscape for advancements in care.