Search results for: requirement%20engineering

Authors: Amandeep Thakur, Yi-Hsuan Chu, Chun-Hsu Pan, Kunal Nepali

Abstract:

The transfer of ADP-ribose residues onto target substrates from nicotinamide adenine dinucleotide (NAD) (PARylation) is catalyzed by Poly (ADP-ribose) polymerases (PARPs). Amongst the PARP family members, the DNA damage response in cancer is majorly regulated by PARP1 and PARP2. The blockade of DNA repair by PARP inhibitors leads to the progression of DNA single-strand breaks (induced by some triggering factors) to double-strand breaks. Notably, PARP inhibitors are remarkably effective in cancers with defective homologous recombination repair (HRR). In particular, cancer cells with BRCA mutations are responsive to therapy with PARP inhibitors. The aforementioned requirement for PARP inhibitors to be effective confers a narrow activity spectrum to PARP inhibitors, which hinders their clinical applicability. Thus, the quest to expand the application horizons of PARP inhibitors beyond BRCA mutations is the need of the hour. Literature precedents reveal that HDAC inhibition induces BRCAness in cancer cells and can broaden the therapeutic scope of PARP inhibitors. Driven by such disclosures, dual inhibitors targeting both PARP and HDAC enzymes were designed by our research group to extend the efficacy of PARP inhibitors beyond BRCA-mutated cancers to cancers with induced BRCAness. The design strategy involved the installation of Veliparib, an investigational PARP inhibitor, as a surface recognition part in the HDAC inhibitor pharmacophore model. The chemical architecture of veliparib was deemed appropriate as a starting point for the generation of dual inhibitors by virtue of its size and structural flexibility. A validatory docking study was conducted at the outset to predict the binding mode of the designed dual modulatory chemical architectures. Subsequently, the designed chemical architectures were synthesized via a multistep synthetic route and evaluated for antitumor efficacy. Delightfully, one compound manifested impressive anti-leukemic effects (HL-60 cell lines) mediated via dual inhibition of PARP and class I HDACs. The outcome of the western blot analysis revealed that the compound could downregulate the expression levels of PARP1 and PARP2 and the HDAC isoforms (HDAC1, 2, and 3). Also, the dual PARP-HDAC inhibitor upregulated the protein expression of the acetyl histone H3, confirming its abrogation potential for class I HDACs. In addition, the dual modulator could arrest the cell cycle at the G0/G1 phase and induce autophagy. Further, polymer-based nanoformulation of the dual inhibitor was furnished to afford targeted delivery of the dual inhibitor at the cancer site. Transmission electron microscopy (TEM) results indicate that the nanoparticles were monodispersed and spherical. Moreover, the polymeric nanoformulation exhibited an appropriate particle size. Delightfully, pH-sensitive behavior was manifested by the polymeric nanoformulation that led to selective antitumor effects towards the HL-60 cell lines. In light of the magnificent anti-leukemic profile of the identified dual PARP-HDAC inhibitor, in-vivo studies (pharmacokinetics and pharmacodynamics) are currently being conducted. Notably, the optimistic findings of the aforementioned study have spurred our research group to initiate several medicinal chemistry campaigns to create bifunctional small molecule inhibitors addressing PARP as the primary target.

Keywords: PARP inhibitors, HDAC inhibitors, BRCA mutations, leukemia

Procedia PDF Downloads 8

Authors: Jeremie Coullon, Robert J. Webber

Abstract:

We introduce a Markov chain Monte Carlo (MCMC) sam-pler for infinite-dimensional inverse problems. Our sam-pler is based on the affine invariant ensemble sampler, which uses interacting walkers to adapt to the covariance structure of the target distribution. We extend this ensem-ble sampler for the first time to infinite-dimensional func-tion spaces, yielding a highly efficient gradient-free MCMC algorithm. Because our ensemble sampler does not require gradients or posterior covariance estimates, it is simple to implement and broadly applicable. In many Bayes-ian inverse problems, Markov chain Monte Carlo (MCMC) meth-ods are needed to approximate distributions on infinite-dimensional function spaces, for example, in groundwater flow, medical imaging, and traffic flow. Yet designing efficient MCMC methods for function spaces has proved challenging. Recent gradi-ent-based MCMC methods preconditioned MCMC methods, and SMC methods have improved the computational efficiency of functional random walk. However, these samplers require gradi-ents or posterior covariance estimates that may be challenging to obtain. Calculating gradients is difficult or impossible in many high-dimensional inverse problems involving a numerical integra-tor with a black-box code base. Additionally, accurately estimating posterior covariances can require a lengthy pilot run or adaptation period. These concerns raise the question: is there a functional sampler that outperforms functional random walk without requir-ing gradients or posterior covariance estimates? To address this question, we consider a gradient-free sampler that avoids explicit covariance estimation yet adapts naturally to the covariance struc-ture of the sampled distribution. This sampler works by consider-ing an ensemble of walkers and interpolating and extrapolating between walkers to make a proposal. This is called the affine in-variant ensemble sampler (AIES), which is easy to tune, easy to parallelize, and efficient at sampling spaces of moderate dimen-sionality (less than 20). The main contribution of this work is to propose a functional ensemble sampler (FES) that combines func-tional random walk and AIES. To apply this sampler, we first cal-culate the Karhunen–Loeve (KL) expansion for the Bayesian prior distribution, assumed to be Gaussian and trace-class. Then, we use AIES to sample the posterior distribution on the low-wavenumber KL components and use the functional random walk to sample the posterior distribution on the high-wavenumber KL components. Alternating between AIES and functional random walk updates, we obtain our functional ensemble sampler that is efficient and easy to use without requiring detailed knowledge of the target dis-tribution. In past work, several authors have proposed splitting the Bayesian posterior into low-wavenumber and high-wavenumber components and then applying enhanced sampling to the low-wavenumber components. Yet compared to these other samplers, FES is unique in its simplicity and broad applicability. FES does not require any derivatives, and the need for derivative-free sam-plers has previously been emphasized. FES also eliminates the requirement for posterior covariance estimates. Lastly, FES is more efficient than other gradient-free samplers in our tests. In two nu-merical examples, we apply FES to challenging inverse problems that involve estimating a functional parameter and one or more scalar parameters. We compare the performance of functional random walk, FES, and an alternative derivative-free sampler that explicitly estimates the posterior covariance matrix. We conclude that FES is the fastest available gradient-free sampler for these challenging and multimodal test problems.

Keywords: Bayesian inverse problems, Markov chain Monte Carlo, infinite-dimensional inverse problems, dimensionality reduction

Procedia PDF Downloads 152