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Summary Small protein ligands can provide superior physiological distribution versus antibodies and improved stability, production, and specific conjugation. Systematic evaluation of the Protein Data Bank identified a scaffold to push the limits of small size and robust evolution of stable, high-affinity ligands: 45-residue T7 phage gene 2 protein (Gp2) contains an α-helix opposite a β-sheet with two adjacent loops amenable to mutation. De novo ligand discovery from 108 mutants and directed evolution towards four targets yielded target-specific binders with affinities as strong as 200 ±100 pM, Tm’s from 65 ±3 °C to 80 ±1 °C, and retained activity after thermal denaturation. For cancer targeting, a Gp2 domain for epidermal growth factor receptor was evolved with 18 ±8 nM affinity, receptor-specific binding, and high thermal stability with refolding. The efficiency of evolving new binding function and the size, affinity, specificity, and stability of evolved domains render Gp2 a uniquely effective ligand scaffold.

A supercapacitor electrode is fabricated with Co0.85Se hollow nanowires (HNW) array, which is synthesized by wet chemical hydrothermal selenization of initially grown cobalt hydroxyl carbonate nanowires on conductive CFP. The dense self-organized morphology of Co0.85Se HNWs is revealed by scanning/transmission electron microscopy. The as-synthesized Co0.85Se HNWs possess high pseudocapacitive property with high capacitance retention and high durability. The areal capacitance value is seen to vary from 929.5 to 600 mF cm(-2) (60% retention) as the current density is increased from 1 to 15 mA cm(-2), an increase of a factor of 15. Based on mass loading, this corresponds to a very high gravimetric capacitance of 674 (for 2 mA cm(-2) or 1.48 Ag(-1)) and 444 Fg(1-) (for 15 mA cm(-2) or 11 A g(-1)) in a full-cell configuration with the Co0.85Se HNWs as cathode and activated carbon as anode (asymmetric configuration) promising results are obtained.

A computational model predicting bystander payload distribution as a function of controllable design parameters for guiding efficient clinical ADC development.

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno‐oncology (IO) the aim is to direct the patient’s own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD‐L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug‐development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds’ pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

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Molecular imaging agent design involves simultaneously optimizing multiple probe properties. While several desired characteristics are straightforward, including high affinity and low non-specific background signal, in practice there are quantitative trade-offs between these properties. These include plasma clearance, where fast clearance lowers background signal but can reduce target uptake, and binding, where high affinity compounds sometimes suffer from lower stability or increased non-specific interactions. Further complicating probe development, many of the optimal parameters vary depending on both target tissue and imaging agent properties, making empirical approaches or previous experience difficult to translate. Here, we focus on low molecular weight compounds targeting extracellular receptors, which have some of the highest contrast values for imaging agents. We use a mechanistic approach to provide a quantitative framework for weighing trade-offs between molecules. Our results show that specific target uptake is well-described by quantitative simulations for a variety of targeting agents, whereas non-specific background signal is more difficult to predict. Two in vitro experimental methods for estimating background signal in vivo are compared – non-specific cellular uptake and plasma protein binding. Together, these data provide a quantitative method to guide probe design and focus animal work for more cost-effective and time-efficient development of molecular imaging agents.