Clonal analysis of gene loss of function and tissue-specific gene deletion in zebrafish via CRISPR/Cas9 technology.

In the last few years the development of CRISPR/Cas 9-mediated genome editing techniques has allowed the efficient generation of loss-of-function alleles in several model organisms including zebrafish. However, these methods are mainly devoted to target-specific genomic loci leading to the creation of constitutive knock-out models. On the contrary, the analysis of gene function via tissue- or cell-specific mutagenesis remains challenging in zebrafish. To circumvent this limitation, we present here a simple and versatile protocol to achieve tissue-specific gene disruption based on the Cas9 expression under the control of the Gal4/upstream activating sequence binary system. In our method, we couple Cas9 with green fluorescent protein or Cre reporter gene expression. This strategy allows us to induce somatic mutations in genetically labeled cell clones or single cells, and to follow them in vivo via reporter gene expression. Importantly, because none of the tools that we present here are restricted to zebrafish, similar approaches are readily applicable in virtually any organism where transgenesis and DNA injection are feasible.

A minimal model of tumor growth inhibition in combination regimens under the hypothesis of no interaction between drugs.

One important issue in the preclinical development of an anticancer drug is the assessment of the compound under investigation when administered in combination with other drugs. Several experiments are routinely conducted in xenograft mice to evaluate if drugs interact or not. Experimental data are generally qualitatively analyzed on empirical basis. The ability of deriving from single drug experiments a reference response to the joint administrations, assuming no interaction, and comparing it to real responses would be key to recognize synergic and antagonist compounds. Therefore, in this paper, the minimal model of tumor growth inhibition (TGI), previously developed for a single drug, is reformulated to account for the effects of noninteracting drugs and simulate, under this hypothesis, combination regimens. The model is derived from a minimal set of basic assumptions that include and extend those formulated at cellular level for the single drug administration. The tumor growth dynamics is well approximated by the deterministic evolution of its expected value that is obtained through the solution of an ordinary and several partial differential equations. Under suitable assumptions on the cell death process, the model reduces to a lumped parameter model that represents the extension of the very popular Simeoni TGI model to the combined administration of noninteracting drugs.

Testing additivity of anticancer agents in pre-clinical studies: a PK/PD modelling approach.

In clinical oncology, combination regimens may result in a synergistic, additive or antagonistic interaction (i.e. the effect of the combination is greater, similar or smaller than the sum of the effects of the individual compounds). For this reason, during the drug development process, in vivo pre-clinical studies are performed to assess the interaction of anticancer agents given in combination. Starting from a widely used single compound PK/PD model, a new additivity model able to predict the tumour growth inhibition in xenografted mice after the administration of compounds in combination was developed, under the assumption of a pharmacodynamic null interaction. By comparing the predicted curves with actual tumour weight data, possible departures from additivity can be immediately ascertained by visual inspection; a statistical procedure based on a chi(2) test has also been developed for this aim. The advantages of the proposed approach in comparison to other modelling methodologies are discussed and its application to four combination studies is presented.