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Biochemical screening is a major source of lead generation for novel targets. However, during the process of small molecule lead optimization, compounds with excellent biochemical activity may show poor cellular potency, making structure-activity relationships difficult to decipher. This may be due to low membrane permeability of the molecule, resulting in insufficient intracellular drug concentration. The Cell Squeeze platform increases permeability regardless of compound structure by mechanically disrupting the membrane, which can overcome permeability limitations and bridge the gap between biochemical and cellular studies. In this study, we show that poorly permeable Janus kinase (JAK) inhibitors are delivered into primary cells using Cell Squeeze, inhibiting up to 90% of the JAK pathway, while incubation of JAK inhibitors with or without electroporation had no significant effect. We believe this robust intracellular delivery approach could enable more effective lead optimization and deepen our understanding of target engagement by small molecules and functional probes.

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Ethereum is one of the most popular platforms for the development of blockchain-powered applications. These applications are known as ÐApps. When engineering ÐApps, developers need to translate requests captured in the front-end of their application into one or more smart contract transactions. Developers need to pay for these transactions and, the more they pay (i.e., the higher the gas price), the faster the transaction is likely to be processed. Developing cost-effective ÐApps is far from trivial, as developers need to optimize the balance between cost (transaction fees) and user experience (transaction processing times). Online services have been developed to provide transaction issuers (e.g., ÐApp developers) with an estimate of how long transactions will take to be processed given a certain gas price. These estimation services are crucial in the Ethereum domain and several popular wallets such as Metamask rely on them. However, despite their key role, their accuracy has not been empirically investigated so far. In this paper, we quantify the transaction processing times in Ethereum, investigate the relationship between processing times and gas prices, and determine the accuracy of state-of-the-practice estimation services. Our results indicate that transactions are processed in a median of 57s and that 90% of the transactions are processed within 8m. We also show that higher gas prices result in faster transaction processing times with diminishing returns. In particular, we observe no practical difference in processing time between expensive and very expensive transactions. With regards to the accuracy of processing time estimation services, we observe that they are equivalent. However, when stratifying transactions by gas prices, we observe that Etherscan’s Gas Tracker is the most accurate estimation service for very cheap and cheap transaction. EthGasStation’s Gas Price API, in turn, is the most accurate estimation service for regular, expensive, and very expensive transactions. In a post-hoc study, we design a simple linear regression model with only one feature that outperforms the Gas Tracker for very cheap and cheap transactions and that performs as accurately as the EthGasStation model for the remaining categories. Based on our findings, ÐApp developers can make more informed decisions concerning the choice of the gas price of their application-issued transactions.

The Ethereum platform allows developers to implement and deploy applications called ÐApps onto the blockchain for public use through the use of smart contracts. To execute code within a smart contract, a paid transaction must be issued towards one of the functions that are exposed in the interface of a contract. However, such a transaction is only processed once one of the miners in the peer-to-peer network selects it, adds it to a block, and appends that block to the blockchain This creates a delay between transaction submission and code execution. It is crucial for ÐApp developers to be able to precisely estimate when transactions will be processed, since this allows them to define and provide a certain Quality of Service (QoS) level (e.g., 95% of the transactions processed within 1 minute). However, the impact that different factors have on these times have not yet been studied. Processing time estimation services are used by ÐApp developers to achieve predefined QoS. Yet, these services offer minimal insights into what factors impact processing times. Considering the vast amount of data that surrounds the Ethereum blockchain, changes in processing times are hard for ÐApp developers to predict, making it difficult to maintain said QoS. In our study, we build random forest models to understand the factors that are associated with transaction processing times. We engineer several features that capture blockchain internal factors, as well as gas pricing behaviors of transaction issuers. By interpreting our models, we conclude that features surrounding gas pricing behaviors are very strongly associated with transaction processing times. Based on our empirical results, we provide ÐApp developers with concrete insights that can help them provide and maintain high levels of QoS.