DSCN: double-target selection guided by CRISPR screening and network

Cancer is a complex disease with usually multiple disease mechanisms. Target combination is a better strategy than a single target in developing cancer therapies. However, target combinations are generally more difficult to be predicted. Current CRISPR-cas9 technology enables genome-wide screening for potential targets, but only a handful of genes have been screend as target combinations. Thus, an effective computational approach for selecting candidate target combinations is highly desirable. Selected target combinations also need to be translational between cell lines and cancer patients.We have therefore developed DSCN (double-target selection guided by CRISPR screening and network), a method that matches expression levels in patients and gene essentialities in cell lines through spectral-clustered protein-protein interaction (PPI) network. In DSCN, a sub-sampling approach is developed to model first-target knockdown and its impact on the PPI network, and it also facilitates the selection of a second target. Our analysis first demonstrated high correlation of the DSCN sub-sampling-based gene knockdown model and its predicted differential gene expressions using observed gene expression in 22 pancreatic cell lines before and after MAP2K1 and MAP2K2 inhibition (R2 = 0.75). In our DSCN algorithm, various scoring schemes were evaluated. The ‘diffusion-path’ method showed the most significant statistical power of differentialting known synthetic lethal (SL) versus non-SL gene pairs (P = 0.001) in pancreatic cancer. The superior performance of DSCN over existing network-based algorithms, such as OptiCon[1] and VIPER[2], in the selection of target combinations is attributable to its ability to calculate combinations for any gene pairs, whereas other approaches focus on the combinations among optimized regulators in the network. DSCN’s computational speed is also at least ten times faster than that of other methods. Finally, in applying DSCN to predict target combinations and drug combinations for individual samples (DSCNi), we showed high correlation of DSCNi predicted target combinations with synergistic drug combinations (P = 1e-5) in pancreatic cell lines. In summary, DSCN is a highly effective computational method for the selection of target combinations.Author SummaryCancer therapies require targets to function. Compared to single target, target combination is a better strategy for developing cancer therapies. However, predicting target combination is much complicated than predicting single target. Current CRISPR technology enables whole genome screening of potential targets. But most of the experiments have been conducted on single target (gene) level. To facilitate the prediction of target combinations, we developed DSCN (double-target selection guided by CRISPR screening and network) that utilize single target-level CRISPR screening data and expression profiles for predicting target combinations by connecting cell-line omics-data with tissue omics-data. DSCN showed great accuracy on different cancer types and superior performance compared to existing network-based prediction tools. We also introduced DSCNi derived from DSCN and designed specific for predicting target combinations for single-paitent. We showed synergistic target combinations predicted by DSCNi accurately reflected synergies on drug combination levels. Thus, DSCN and DSCNi have the potential be further applied in personalized medicine field.

A surface-free design for phase I dual-agent combination trials

In oncology, there is a growing number of therapies given in combination. Recently, several dose-finding designs for Phase I dose-escalation trials for combinations were proposed. The majority of novel designs use a pre-specified parametric model restricting the search of the target combination to a surface of a particular form. In this work, we propose a novel model-free design for combination studies, which is based on the assumption of monotonicity within each agent only. Specifically, we parametrise the ratios between each neighbouring combination by independent Beta distributions. As a result, the design does not require the specification of any particular parametric model or knowledge about increasing orderings of toxicity. We compare the performance of the proposed design to the model-based continual reassessment method for partial ordering and to another model-free alternative, the product of independent beta design. In an extensive simulation study, we show that the proposed design leads to comparable or better proportions of correct selections of the target combination while leading to the same or fewer average number of toxic responses in a trial.

Investigations on 54–60Fe + 238–244Pu → 296–302120 fusion reactions

We have studied the 54–60Fe-induced fusion reactions to synthesize the superheavy nuclei296–302120 by studying the compound nucleus formation probability, survival probability, and evaporation residue cross-sections. The comparison of the evaporation residue cross-section for different targets reveals that the evaporation residue cross-section is larger for projectile target combination 58Fe+243Pu→301120. We have identified the most probable 58Fe-induced fusion reactions to synthesize superheavy nuclei 296–302120. The suggested reactions may be useful to synthesize the superheavy element Z = 120.

Molecular Hybridization as a Tool for Designing Multitarget Drug Candidates for Complex Diseases

Molecular hybridization is a well-exploited medicinal chemistry strategy that aims to combine two molecules (or parts of them) in a new, single chemical entity. Recently, it has been recognized as an effective approach to design ligands able to modulate multiple targets of interest. Hybrid compounds can be obtained by linking (presence of a linker) or framework integration (merging or fusing) strategies. Although very promising to combat the multifactorial nature of complex diseases, the development of molecular hybrids faces the critical issues of selecting the right target combination and the achievement of a balanced activity towards them, while maintaining drug-like-properties. In this review, we present recent case histories from our own research group that demonstrate why and how molecular hybridization can be carried out to address the challenges of multitarget drug discovery in two therapeutic areas that are Alzheimer’s and parasitic diseases. Selected examples spanning from linker- to fragment- based hybrids will allow to discuss issues and consequences relevant to drug design.

Deep Reinforcement Learning with Adaptive Update Target Combination

Simple and efficient exploration remains a core challenge in deep reinforcement learning. While many exploration methods can be applied to high-dimensional tasks, these methods manually adjust exploration parameters according to domain knowledge. This paper proposes a novel method that can automatically balance exploration and exploitation, as well as combine on-policy and off-policy update targets through a dynamic weighted way based on value difference. The proposed method does not directly affect the probability of a selected action but utilizes the value difference produced during the learning process to adjust update target for guiding the direction of agent’s learning. We demonstrate the performance of the proposed method on CartPole-v1, MountainCar-v0, and LunarLander-v2 classic control tasks from the OpenAI Gym. Empirical evaluation results show that by integrating on-policy and off-policy update targets dynamically, this method exhibits superior performance and stability than does the exclusive use of the update target.