Pharmacokinetic profiles of cancer sonochemotherapy

<p><b>Introduction:</b> Sonochemotherapy is a promising strategy for the treatment of cancer, however, there is limited understanding of its pharmacokinetics (PK).</p> <p><b>Area covered:</b> The PK profile of sonochemotherapy is evaluated based on released data. Preclinical investigations suggest that the blood PK of sonochemotherapy is similar to chemotherapy when using free anticancer drugs. When using encapsulated drugs, a lower plasma level usually occurs; however, the ultrasonic release of drugs within a tumor may lead to drugs leaking into circulation, causing a rebound in the plasma drug level; a higher drug level is detected in certain healthy organs, however this depends mostly on the pharmaceutical formulation. Sonochemotherapy increases both the level and retention time of drugs in a tumor. Clinical trials of combined chemotherapy and high intensity focused ultrasound (HIFU) are evaluated from the perspective of preclinical PK: the intratumoral PK and drug interactions under insonation, and a protocol to set the interval between drug administration and insonation are lacking.</p> <p><b>Expert opinion:</b> Insonation can alter the PK properties of chemotherapeutics, which may exacerbate the system and/or organ toxicity of anticancer drugs. Directly employing the PK parameters validated in conventional chemotherapy plays an important role in unsatisfactory clinical outcomes of chemotherapy combined with HIFU.</p

Binding Affinity Prediction for Protein–Ligand Complexes Based on <i>β</i> Contacts and B Factor

Accurate determination of protein–ligand binding affinity is a fundamental problem in biochemistry useful for many applications including drug design and protein–ligand docking. A number of scoring functions have been proposed for the prediction of protein–ligand binding affinity. However, accurate prediction is still a challenging problem because poor performance is often seen in the evaluation under the leave-one-cluster-out cross-validation (LCOCV). We introduce a new scoring function named B2BScore to improve the prediction performance. B2BScore integrates two physicochemical properties for protein–ligand binding affinity prediction. One is the property of <i>β</i> contacts. A <i>β</i> contact between two atoms requires no other atoms to interrupt the atomic contact and assumes that the two atoms should have enough direct contact area. The other is the property of B factor to capture the atomic mobility in the dynamic protein–ligand binding process. Tested on the PDBBind2009 data set, B2BScore shows superior prediction performance to existing methods on independent test data as well as under the LCOCV evaluation framework. In particular, B2BScore achieves a significant LCOCV improvement across 26 protein clustersa big increase of the averaged Pearson’s correlation coefficients from 0.418 to 0.518 and a significant decrease of standard deviation of the coefficients from 0.352 to 0.196. We also identified several important and intuitive contact descriptors of protein–ligand binding through the random forest learning in B2BScore. Some of these descriptors are closely related to contacts between carbon atoms without covalent-bond oxygen/nitrogen, preferred contacts of metal ions, interfacial backbone atoms from proteins, or π rings. Some others are negative descriptors relating to those contacts with nitrogen atoms without covalent-bond hydrogens or nonpreferred contacts of metal ions. These descriptors can be directly used to guide protein–ligand docking

The distribution of top three features V14 (in (a)), V34 (in (b)) and V84 (in (c)).

<p>The definitions of V14, V34 and V84 are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144486#pone.0144486.t005" target="_blank">Table 5</a>. The importance of V14, V34 and V84 is ranked as 1^<i>st</i>, 2^<i>nd</i> and 3^<i>rd</i>, respectively, as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144486#pone.0144486.g002" target="_blank">Fig 2</a>, while the Pearson correlation coefficients of the three features are -0.315, -0.277 and -0.273, respectively, as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144486#pone.0144486.t005" target="_blank">Table 5</a>. The y-axes denote ΔΔ<i>G</i>. ∘: ILE, VAL, LEU, MET, ALA and GLY; ◻: CYS, THR, SER, PRO, HIS, GLN and ASN; ♢: GLU, ASP, LYS and ARG; ▿: PHE, TRP and TYR. (d) shows an example of the neighborhood of the two mutations (in brown): Tyr54 and Tyr55 of Chain A (in red) in 1BXI together with the partner protein (Chain B in green). All carbon atoms in the neighborhood which have no covalent bond with any oxygen or nitrogen are shown in ‘sphere’ view. The alanine mutation of Tyr54 has ΔΔ<i>G</i> = 4.83kcal/mol with the smallest value of V84, and that of Tyr55 has ΔΔ<i>G</i> = 4.63kcal/mol with the smallest value of V34.</p

The distribution of Bdifr for the fourth amino acid group (in (a)), and for the third group (in (b)), and for the first and second groups (in (d)), and the distribution of Bavgr for the third group (in (c)).

<p>The y-axes denote ΔΔ<i>G</i>. ∘: ILE, VAL, LEU, MET, ALA and GLY; ◻: CYS, THR, SER, PRO, HIS, GLN and ASN; ♢: GLU, ASP, LYS and ARG; ▿: PHE, TRP and TYR. The importance of V10, V8 and V7 is ranked as 9^<i>th</i>, 19^<i>th</i> and 24^<i>th</i>, respectively, as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144486#pone.0144486.g002" target="_blank">Fig 2</a>, while the Pearson correlation coefficients of the three features are -0.054, -0.213 and -0.313, respectively, as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144486#pone.0144486.t005" target="_blank">Table 5</a>. V4 and V6 are not in the top 40 important features in randomForest.</p

Top-ranking features useful for protein binding hot spot prediction by random forest.

<p>‘%IncMSE’ indicates the increase of the mean standard error (MSE) after the permutation of the features. The definitions of the top 25 important features are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144486#pone.0144486.t005" target="_blank">Table 5</a>.</p

The binding hot spots [35] (in magenta) unique to 1TH1 (Chain A in green and Chain C in red in (a)), 1JPP (Chain A in green and Chain C in red in (b)) and 3OUX (Chain A in green and Chain B in red in (c)).

<p>There is an overlapping common area to the interfaces of these protein complexes; the chains in green are the same, but the binding partner proteins in red are different. The binding hot spots in Chain A (Lys-345 and Trp-383 in (a), Arg-386 in (b), and Lys-435 and His-470 (c)) are all in a ‘spheres’ view.</p

Tricyclic Diester and 2,5-Furandicarboxylic Acid for the Synthesis of Biobased Hydrolysis Copolyesters with High Glass Transition Temperatures

The reluctance of a polyester with high glass transition temperature (Tg) and mechanical properties to hydrolyze is a well-known fact, for instance, the high hydrolysis resistance of aromatic polyesters based on terephthalic acid and 2,5-furandicarboxylic acid (FDCA). The synthesis of polyesters that have a high Tg (>100 °C) and a fast hydrolytic degradation quality at the same time is a valuable topic. Herein, a renewable rigid diester, N,N′-trans-1,4-cyclohexane-bis(pyrrolidone-4-methyl carboxylate) (CBPC), was obtained via Michael addition. CBPC was copolymerized with FDCA and ethylene glycol to prepare a series of copolyesters PECxEFy with a high Mn over 30 kDa. PECxEFy showed a Tg range of 75.2–109.2 °C which outdistanced the most biobased polyesters. The thermal stability of all PECxEFy remained unchanged with the introduction of CBPC. Moreover, PECxEFy presented superior mechanical performances which were matching or exceeding those of commercial polyethylene terephthalate (PET) and polylactic acid (PLA). PECxEFy was stable in air but was able to undergo noticeable hydrolytic degradation, proving their enhanced degradability. And the regulation between CBPC and FDCA composition can be leveraged to adjust the degradation and environmental durability of PECxEFy, up to practical applications. Computational studies systematically revealed the relationship between CBPC with a tricyclic structure and the improved Tg and hydrolyzation properties. The outstanding thermal and mechanical performances and hydrolysis of these copolyesters appear to be promising candidates for renewable alternatives to industrial petrochemical polyesters

Multiple interfaces in the catalytic domain of plasmin.

<p>The interfaces are colored in limegreen (46 residues), marine (6 residues), and red (7 residues) for plasmin interacting with a streptokinase, a protein inhibitor, and another plasmin symmetric unit, respectively. The two overlapping residues are colored orange, and the five molecular functions of this domain retrieved from GO are shown at the top right corner.</p

Distribution of multi-interface domains at different SCOP classification levels.

•<p>number of clusters under the given SCOP classification level;</p>°<p>number of clusters that have multi-interface proteins under the given SCOP classification level;</p>†<p>number of protein chains that have multi-interfaces;</p>‡<p>data is not available in SCOP;</p>*<p>the total number of domains is 97,178 in SCOP version 1.73, but the total number of domains listed above is slightly smaller since there are still four classes with very few number of domains are not shown here.</p><p>SCOP version 1.73 instead of 1.75 is used in this study because the PDBeFold <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050821#pone.0050821-Krissinel1" target="_blank">[34]</a> is based on SCOP version 1.73, which is used to search SCOP to get similar domains for a given protein.</p

Multi-interface protein distribution in terms of the number of multiple interfaces.

<p>Multi-interface protein distribution in terms of the number of multiple interfaces.</p