Don’t play roulette with your clinical vector candidates: rethinking vector design for successful gene therapy

In today’s fast-paced, high-stakes world of cell and gene therapy (CGT), where capital is fleeting and timelines are tight, success favors those who treat vector candidate selection not as an academic gamble, but as a scalar science. The past two decades for advanced therapy medicinal products (ATMPs) have laid bare a jarring duality: on one hand, highly investable and transformative emerging therapies, but on the other, tranches of developers chasing crowded indications with limited differentiation between actual product treatments, and many repeating the same mistake of rushed candidate selection.

Intensive pressure emerges as therapies targeting the same diseases race to trials, shifting focus to how quickly a therapy can move toward the finish line and disregarding red flags that foretell failure along the way. Investors target a potential therapy just after proof-of-concept (POC) of a single candidate with limited access to realistic indicators of success, so bets are placed early on, and inevitable hurdles are pushed past as quickly as possible. The result is a candidate that stumbles or falls in preclinical toxicology or full-scale GMP manufacturing due to issues that could have been predicted or prevented way upstream. The adage ‘start with the end in mind’ is often invoked to portray holistic thinking, but in practice represents a poorly executed aspiration. In truth, only a handful of developers truly have the depth of experience (e.g., millions of vector designs to draw upon) and the capabilities required to genuinely ‘start with the end in mind’.

The solution lies not in speed, but rather in placing smarter, broader bets on candidate vectors. Betting everything on a single construct chosen too early and without sufficient rigor is like putting all your chips on a single number on the roulette wheel and hoping for the best. For those hoping to find success in CGT, we require more than speed and willingness to place high-stakes bets; we must be deliberate from the start. This sounds simple and sensical, but for developers without deep experience in vector design or empirical data, it is their key challenge.

Please enter your email to access content Read now