Al in Biotechnology Market

DRIVER: Growing need to reduce the time and cost of drug discovery and development.

Drug discovery in biotechnology is a very costly and lengthy process, owing to which there is a need for alternative tools for discovering new drugs. Even though in vitro and in vivo methods are expensive and lengthy, they are generally applied in drug discovery & development. Developing a new medicine usually takes 10–15 years and costs up to USD 2.8 billion on average. (Source: ScienceDirect). Although the number of new molecular entities (NMEs) approved by regulatory agencies, such as the US Food and Drug Administration (FDA), has increased over the past decade (2010-2019) compared with the prior decade, the cost of bringing a new drug to market has risen precipitously. The key causes of increasing costs for pharmaceutical innovation include losses from investments in later-stage clinical failures, a stringent regulatory environment that demands higher standards for approval, and inflated clinical trial costs where the most expensive trials are generally those of pivotal studies. Under these conditions, drug and biotechnology companies are driven to innovate and integrate new technologies to increase productivity, decrease costs, and ensure profitability.

Most drug candidates selected in the discovery phase fail in the late stages of development due to toxicity or other pharmacokinetic characteristics. Machine learning technology can help at this stage by predicting the outcome of a drug compound in the discovery phase and eliminating compounds without potential in the early discovery phase itself. This will drastically reduce time and costs associated with finding possible medication candidates.

RESTRAINT: High implementation costs of AI limit adoption in biotechnology, especially for SMEs and emerging economies

Despite Al's revolutionary promise in biotechnology, a significant barrier is the expensive expense of implementing and maintaining complex Al systems. The costs associated with these solutions, including subscription and licensing fees, can range from USD 10,000 to several million dollars, depending on various factors such as application areas, solution types, and data volume. For many biotechnology companies, especially small- and medium-sized enterprises (SMEs) and contract research organizations (CROs), these high costs are prohibitive. The financial impact is particularly noticeable in emerging economies since budgets often set greater importance on investing on medical equipment than on IT. Due to this, advanced AI technologies are not typically employed in these areas, which restricts the potential of big data for useful evaluation by smaller businesses.