Predicting the clinical subcutaneous absorption rate constant of monoclonal antibodies using only the primary sequence: a machine learning approach.

Subcutaneous injections are an increasingly prevalent route of administration for delivering biological therapies including monoclonal antibodies (mAbs). Compared with intravenous delivery, subcutaneous injections reduce administration costs, shorten the administration time, and are strongly preferred from a patient experience point of view. An understanding of the absorption process of a mAb from the injection site to the systemic circulation is critical to the process of subcutaneous mAb formulation development. In this study, we built a model to predict the absorption rate constant (ka), which denotes how fast a mAb is absorbed from the site of administration. Once trained, our model (enabled by the XGBoost algorithm in machine learning) can predict the ka of a mAb following a subcutaneous injection using in silico molecular properties alone (generated from the primary sequence). Our model does not need clinically observed plasma concentration-time data; this is a novel capability not previously achieved in predictive pharmacokinetic models. The model also showed improved performance when benchmarked against a recently reported mechanistic model that relied on clinical data to predict subcutaneous absorption of mAbs. We further interpreted the model to understand which molecular properties affect the absorption rate and showed that our findings are consistent with previous studies evaluating subcutaneous absorption through direct experimentation. Taken altogether, this study reports the development, validation, benchmarking, and interpretation of a model that can predict the clinical ka of a mAb using its primary sequence as the only input.

CVD for the facile synthesis of hybrid nanobiomaterials integrating functional supramolecular assemblies.

In this letter, we present a simple one-step, versatile, scalable chemical vapor deposition (CVD)-based process for the encapsulation and stabilization of a host of single or multicomponent supramolecular assemblies (proteoliposomes, microbubbles, lipid bilayers, and photosynthetic antennae complexes and other biological materials) to form functional hybrid nanobiomaterials. In each case, it is possible (i) to form thin silica layers or gels controllably that enable the preservation of the supramolecular assembly over time and under adverse environmental conditions and (ii) to tune the structure of the silica gels so as to optimize solute accessibility while at the same time preserving functional dynamic properties of the encapsulated phospholipid assembly. The process allows precise temporal and spatial control of silica polymerization kinetics through the control of precursor delivery at room temperature and does not require or produce high concentrations of injurious chemicals that can compromise the function of biomolecular assemblies; it also does not require additives. This process differs from the conventional sol-gel process in that it does not involve the use of cosolvents (alcohols) and catalysts (acid or base).