Tang Yifeng, Kim Jeremiah Y, Ip Carman K M, Bahmani Azadeh, Chen Qing, Rosenberger Matthew G, Esser-Kahn Aaron P, Ferguson Andrew L
Pritzker School of Molecular Engineering, University of Chicago Chicago IL 60637 USA
Cellular Screening Center, University of Chicago Chicago IL 60637 USA.
Chem Sci. 2023 Oct 18;14(44):12747-12766. doi: 10.1039/d3sc03613h. eCollection 2023 Nov 15.
The innate immune response is vital for the success of prophylactic vaccines and immunotherapies. Control of signaling in innate immune pathways can improve prophylactic vaccines by inhibiting unfavorable systemic inflammation and immunotherapies by enhancing immune stimulation. In this work, we developed a machine learning-enabled active learning pipeline to guide experimental screening and discovery of small molecule immunomodulators that improve immune responses by altering the signaling activity of innate immune responses stimulated by traditional pattern recognition receptor agonists. Molecules were tested by high throughput screening (HTS) where we measured modulation of the nuclear factor κ-light-chain-enhancer of activated B-cells (NF-κB) and the interferon regulatory factors (IRF) pathways. These data were used to train data-driven predictive models linking molecular structure to modulation of the NF-κB and IRF responses using deep representational learning, Gaussian process regression, and Bayesian optimization. By interleaving successive rounds of model training and HTS, we performed an active learning-guided traversal of a 139 998 molecule library. After sampling only ∼2% of the library, we discovered viable molecules with unprecedented immunomodulatory capacity, including those capable of suppressing NF-κB activity by up to 15-fold, elevating NF-κB activity by up to 5-fold, and elevating IRF activity by up to 6-fold. We extracted chemical design rules identifying particular chemical fragments as principal drivers of specific immunomodulation behaviors. We validated the immunomodulatory effect of a subset of our top candidates by measuring cytokine release profiles. Of these, one molecule induced a 3-fold enhancement in IFN-β production when delivered with a cyclic di-nucleotide stimulator of interferon genes (STING) agonist. In sum, our machine learning-enabled screening approach presents an efficient immunomodulator discovery pipeline that has furnished a library of novel small molecules with a strong capacity to enhance or suppress innate immune signaling pathways to shape and improve prophylactic vaccination and immunotherapies.
先天免疫反应对于预防性疫苗和免疫疗法的成功至关重要。控制先天免疫途径中的信号传导可以通过抑制不利的全身炎症来改进预防性疫苗，并通过增强免疫刺激来改进免疫疗法。在这项工作中，我们开发了一种基于机器学习的主动学习流程，以指导实验筛选和发现小分子免疫调节剂，这些小分子免疫调节剂通过改变传统模式识别受体激动剂刺激的先天免疫反应的信号传导活性来改善免疫反应。通过高通量筛选（HTS）对分子进行测试，我们在其中测量了活化B细胞的核因子κ轻链增强子（NF-κB）和干扰素调节因子（IRF）途径的调节。这些数据被用于训练数据驱动的预测模型，该模型使用深度表征学习、高斯过程回归和贝叶斯优化将分子结构与NF-κB和IRF反应的调节联系起来。通过交错进行连续几轮的模型训练和HTS，我们对一个包含139998个分子的文库进行了主动学习引导的遍历。在仅对文库的约2%进行采样后，我们发现了具有前所未有的免疫调节能力的可行分子，包括那些能够将NF-κB活性抑制高达15倍、将NF-κB活性提高高达5倍以及将IRF活性提高高达6倍的分子。我们提取了化学设计规则，确定特定的化学片段是特定免疫调节行为的主要驱动因素。我们通过测量细胞因子释放谱验证了我们的顶级候选分子子集的免疫调节作用。其中，一种分子与干扰素基因（STING）激动剂的环状二核苷酸刺激剂一起递送时，可使IFN-β产生增强3倍。总之，我们基于机器学习的筛选方法提供了一种高效的免疫调节剂发现流程，该流程提供了一个新型小分子文库，这些小分子具有强大的能力来增强或抑制先天免疫信号通路，以塑造和改进预防性疫苗接种和免疫疗法。
