Network biology and artificial intelligence drive the understanding of the multidrug resistance phenotype in cancer

Bueschbell, Beatriz; Caniceiro, Ana B.; Suzano, Pedro M S; Machuqueiro, Miguel; Rosário‐Ferreira, Nícia; Moreira, Irina S.

doi:10.1016/j.drup.2022.100811

Cited by 22 publications

(19 citation statements)

References 545 publications

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“…For instance, UGT1A6 has been found to be correlated with resistance to programmed cell death 1 (PD‐1) antibody nivolumab in patients with advanced renal clear‐cell cancer 88 . As UGTs are not known to conjugate proteins like antibodies, UGT‐mediated resistance to nivolumab may not be related to directed drug inactivation, but to other mechanisms, such as indirect metabolism regulations by UGTs and other resistance‐correlated signaling regulations involving UGTs 9 . Because of their association with hampered drug response, the expression of UGT1As might be a useful indicator for patient stratifying so as to avoid the application of substrate drugs to patients who are likely to have low response due to high UGT1A levels.…”

Section: Resistance Mechanisms In Cancer and Combating Strategiesmentioning

confidence: 99%

“…Despite the great development and improvement of cancer treatment in recent decades, resistance to cancer therapies has been a commonly observed phenomenon in clinical practice 7,8 . Moreover, cancer cells with resistant characteristics often exhibited cross‐resistance to a variety of anticancer drugs that can be structurally irrelevant, namely, the multidrug resistance (MDR) phenomenon 9 . MDR has been a major obstacle impeding therapeutic success and a dominating cause of cancer relapse and cancer‐related death.…”

Section: Introductionmentioning

confidence: 99%

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Understanding and targeting resistance mechanisms in cancer

Lei

Teng

Wurpel

et al. 2023

MedComm

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Resistance to cancer therapies has been a commonly observed phenomenon in clinical practice, which is one of the major causes of treatment failure and poor patient survival. The reduced responsiveness of cancer cells is a multifaceted phenomenon that can arise from genetic, epigenetic, and microenvironmental factors. Various mechanisms have been discovered and extensively studied, including drug inactivation, reduced intracellular drug accumulation by reduced uptake or increased efflux, drug target alteration, activation of compensatory pathways for cell survival, regulation of DNA repair and cell death, tumor plasticity, and the regulation from tumor microenvironments (TMEs). To overcome cancer resistance, a variety of strategies have been proposed, which are designed to enhance the effectiveness of cancer treatment or reduce drug resistance. These include identifying biomarkers that can predict drug response and resistance, identifying new targets, developing new targeted drugs, combination therapies targeting multiple signaling pathways, and modulating the TME. The present article focuses on the different mechanisms of drug resistance in cancer and the corresponding tackling approaches with recent updates. Perspectives on polytherapy targeting multiple resistance mechanisms, novel nanoparticle delivery systems, and advanced drug design tools for overcoming resistance are also reviewed.

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Section: Resistance Mechanisms In Cancer and Combating Strategiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding and targeting resistance mechanisms in cancer

Lei

Teng

Wurpel

et al. 2023

MedComm

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“…36 Among these knowledge-basaed pK a calculators, CpHMD gives the most reliable prediction of pK a 's, especially for the proteins that are dynamic. 11,37,38 However, CpHMD is time-consuming and thus unsuitable for mass pK a predictions in the areas of protein 11 and drug 39 designs in industry and protein pK a database construction. 40−42 Recently, artificial intelligence (AI) algorithms were applied to the developments of protein pK a predictors.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the above MD or MC technique, a number of constant pH molecular dynamics (CpHMD) methods have been developed to predict protein p K a ’s and simultaneously explore molecular mechanisms of pH-regulated biological events, including proton-coupled ion transport across cellular membrane, , enzyme catalysis, − and cellulosomal assembly . Among these knowledge-basaed p K a calculators, CpHMD gives the most reliable prediction of p K a ’s, especially for the proteins that are dynamic. ,, However, CpHMD is time-consuming and thus unsuitable for mass p K a predictions in the areas of protein and drug designs in industry and protein p K a database construction. − …”

Section: Introductionmentioning

confidence: 99%

Basis for Accurate Protein pK_a Prediction with Machine Learning

Cai

Liu

Lin

et al. 2023

J. Chem. Inf. Model.

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pH regulates protein structures and the associated functions in many biological processes via protonation and deprotonation of ionizable side chains where the titration equilibria are determined by pK a’s. To accelerate pH-dependent molecular mechanism research in the life sciences or industrial protein and drug designs, fast and accurate pK a prediction is crucial. Here we present a theoretical pK a data set PHMD549, which was successfully applied to four distinct machine learning methods, including DeepKa, which was proposed in our previous work. To reach a valid comparison, EXP67S was selected as the test set. Encouragingly, DeepKa was improved significantly and outperforms other state-of-the-art methods, except for the constant-pH molecular dynamics, which was utilized to create PHMD549. More importantly, DeepKa reproduced experimental pK a orders of acidic dyads in five enzyme catalytic sites. Apart from structural proteins, DeepKa was found applicable to intrinsically disordered peptides. Further, in combination with solvent exposures, it is revealed that DeepKa offers the most accurate prediction under the challenging circumstance that hydrogen bonding or salt bridge interaction is partly compensated by desolvation for a buried side chain. Finally, our benchmark data qualify PHMD549 and EXP67S as the basis for future developments of protein pK a prediction tools driven by artificial intelligence. In addition, DeepKa built on PHMD549 has been proven an efficient protein pK a predictor and thus can be applied immediately to, for example, pK a database construction, protein design, drug discovery, and so on.

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“…1−8 The increased energy demand of the cancer cell requires a boost in the glucose consumption rate, and its conversion into lactates in the presence of oxygen (Warburg effect) has been observed in the tumor cells. 1,4,5,8 The malignant cells extrude the excess protons outside the cell, which results in the formation of an acidic tumor microenvironment (TME) that surrounds the tumor cell and acts as an additional barrier for the passive crossing of the cell membrane by various chemotherapic agents like doxorubicin, mitoxantrone, vincristine, and vinblastine, which are weak Lewis bases. 4,5 These conventional drugs become highly protonated while crossing the TME, leading to a marked decrease in cellular uptake.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Uptake of Anticancer C6-Pep Dimer in a Model Membrane Caused by Differential pK_a in Acidic Microenvironment

Srivastava,

Patra

2023

J. Phys. Chem. B

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Acidic tumor microenvironment (TME) presents a challenge for the action of antitumor drugs by acting as an additional barrier for the passive crossing of the cell membrane by chemotherapic agents playing a critical role in the proliferation of tumor cells. Anticancer lipopeptide C6-Pep dimer containing the leucine zipper motif shows an increased uptake into the model tumor membrane in TME, and application of external heat might lead to the uncoiling of the zipper, which could result in cell lysis. This work investigated the cause of this increased uptake of C6-Pep dimer into the bilayer model in TME. Accurate protonation states of all the titratable residues of the C6-Pep dimer in TME were determined using constant pH molecular dynamics. In TME, except for two terminal Glu5 residues, all other Glu residues in the C6-Pep dimer were permanently protonated. The remaining Glu5 residues had differential pK a values, leading to the construction of four possible dimers with different fixed protonation states, and molecular dynamics was used to study their interaction with the anionic bilayer. Except for the dimer at a physiological pH, the other dimers were positively charged and could readily adsorb on the membrane surface. The free energy of insertion of these dimers in the bilayer was lower for single and double protonated Glu5-containing dimers than for the others. After the insertion of the lipopeptides into the membrane, thinning of the bilayer in the vicinity of dimers and an increase in area per lipid of the bilayer were observed for all systems, indicating destabilization of the bilayer due to this intercalation. This study shows that the anticancer lipopeptide C6-Pep utilizes the TME around a tumor cell for insertion into the membrane.

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Network biology and artificial intelligence drive the understanding of the multidrug resistance phenotype in cancer

Cited by 22 publications

References 545 publications

Understanding and targeting resistance mechanisms in cancer

Understanding and targeting resistance mechanisms in cancer

Basis for Accurate Protein pK_a Prediction with Machine Learning

Enhanced Uptake of Anticancer C6-Pep Dimer in a Model Membrane Caused by Differential pK_a in Acidic Microenvironment

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Network biology and artificial intelligence drive the understanding of the multidrug resistance phenotype in cancer

Cited by 22 publications

References 545 publications

Understanding and targeting resistance mechanisms in cancer

Understanding and targeting resistance mechanisms in cancer

Basis for Accurate Protein pKa Prediction with Machine Learning

Enhanced Uptake of Anticancer C6-Pep Dimer in a Model Membrane Caused by Differential pKa in Acidic Microenvironment

Contact Info

Product

Resources

About

Basis for Accurate Protein pK_a Prediction with Machine Learning

Enhanced Uptake of Anticancer C6-Pep Dimer in a Model Membrane Caused by Differential pK_a in Acidic Microenvironment