Developing a new class of engineered live bacterial therapeutics to treat human diseases

Abstract

A complex interplay of metabolic and immunological mechanisms underlies many diseases that represent a substantial unmet medical need. There is an increasing appreciation of the role microbes play in human health and disease, and evidence is accumulating that a new class of live biotherapeutics comprised of engineered microbes could address specific mechanisms of disease. Using the tools of synthetic biology, nonpathogenic bacteria can be designed to sense and respond to environmental signals in order to consume harmful compounds and deliver therapeutic effectors. In this perspective, we describe considerations for the design and development of engineered live biotherapeutics to achieve regulatory and patient acceptance.

Fig. 1. Considerations for the design of engineered live bacterial therapeutics.

a Several aspects require consideration during the design of an engineered bacterial therapeutic. The selection of a chassis organism can be guided by the desired site of activity and pharmacokinetic properties of the chassis, as well as manufacturing feasibility. The design of genetic circuits may also be influenced by the circuit’s effectors, pragmatic concerns regarding inducer compounds, and the genetic stability of regulatory circuits. Critically, the design of an engineered bacterial drug may also be constrained by considerations for the needs of patients. b Optimal strain design often requires a balance between strain suitability for function in the target microenvironment and concerns for feasibility of manufacturing and clinical development.

Fig. 2. Strategy for the development of engineered live bacterial therapeutic clinical candidates.

Schematic representation of a workflow for developing clinical candidate-quality engineered strains. The development workflow should incorporate technologies for optimizing strain potency, as well as predictive in vitro and in vivo assays, as well quantitative pharmacology models, to maximize translational potential for patient populations.