Straightforward method for calibration of mechanistic cation exchange chromatography models for industrial applications

Mechanistic modeling of chromatography processes is one of the most promising techniques for the digitalization of biopharmaceutical process development. Possible applications of chromatography models range from in silico process optimization in early phase development to in silico root cause investigation during manufacturing. Nonetheless, the cumbersome and complex model calibration still decelerates the implementation of mechanistic modeling in industry. Therefore, the industry demands model calibration strategies that ensure adequate model certainty in a limited amount of time. This study introduces a directed and straightforward approach for the calibration of pH-dependent, multicomponent steric mass action (SMA) isotherm models for industrial applications. In the case investigated, the method was applied to a monoclonal antibody (mAb) polishing step including four protein species. The developed strategy combined well-established theories of preparative chromatography (e.g. Yamamoto method) and allowed a systematic reduction of unknown model parameters to 7 from initially 32. Model uncertainty was reduced by designing two representative calibration experiments for the inverse estimation of remaining model parameters. Dedicated experiments with aggregate-enriched load material led to a significant reduction of model uncertainty for the estimates of this low-concentrated product-related impurity. The model was validated beyond the operating ranges of the final unit operation, enabling its application to late-stage downstream process development. With the proposed model calibration strategy, a systematic experimental design is provided, calibration effort is strongly reduced, and local minima are avoided.     

Comparaison de l’estimation du débit de filtration glomérulaire par le 99mTc-DTPA au radiotraceur de référence le 51Cr-EDTA comparativement aux méthodes basées sur la mesure de la créatininémie

Since the end of 2018, the distribution of the reference tracer for the measurement of glomerular filtration rate (GFR), the 51Cr-EDTA, is no longer provided by radiopharmaceutical companies around the world. In this study, we propose to compare the measurement of glomerular filtration rate by 99mTc-DTPA to that by 51Cr-EDTA. A double estimation of GFR by plasma clearance was performed in 12 patients, 10 of which were referred for GFR calculation prior to possible kidney donation. Linear regression coefficients and intraclass correlation coefficient (ICC) were calculated for the GFR measurement by 99mTc-DTPA, and by MDRD, CKD-EPI and Cockcroft and Gault formulas, relative to the 51Cr-EDTA measurement. The clearance measurement with 99mTc-DTPA is on average 7.25 [2.00; 14.96] mL/min/1.73m² higher than that of 51Cr-EDTA. The GFR measurement with 99mTc-DTPA showed a trend towards better agreement with the 51Cr-EDTA measurement in terms of linear regression parameters, but also in terms of ICC compared to the MDRD, CKD-EPI and Cockcroft and Gault methods. In conclusion, our study supports the use of the 99mTc-DTPA tracer in place of 51Cr-EDTA and shows a higher reliability compared to methods based on blood creatinine measurement.     

A new cloud-based classification methodology (CBCM) for efficient semantic web service discovery

Over the last decades, web services are used for performing specific tasks demanded by users. The most important task of service’s classification system is to match an anonymous input service with the stored pre-classified web services. The most challenging issue is that web services are currently organized and classified according to syntax while the context of the requested service is ignored. Due to this motivation, Cloud-based Classification Methodology is proposed as it presents a new methodology based on semantic web service’s classification. Furthermore, cloud computing is used for not only storing but also allocating the high scale of web services with both high availability and accessibility. Fog technology is employed to reduce the latency and to speed up response time. The experimental results using the suggested methodology show a better performance of the proposed system regarding both precision and accuracy in comparison with most of the methods discussed in the literature of the current study.

     

Resource allocation mechanisms for maximizing provider’s revenue in infrastructure as a service (IaaS) cloud

Infrastructure as a Service (IaaS) is a cloud computing service provided over the internet to facilitate the provisioning of various services such as storage, processes, etc. The provider in the IaaS market may offer some purchasing plans including: reservation, on-demand, and spot plans for its resources. As in real scenarios, demand volume for each plan is assumed to be a random variable with a given probability distribution. The provider maximizes its average revenue in the long run by optimal allocation of its resources among the plans. We formulate an Integer Linear Programming (ILP) model with a stochastic constraint, to determine the number of resources to be allocated for each plan in every time slot in the planning horizon. First, fixed prices are considered for each plan, then two mechanisms of Continuous Double Auction and Second Price Sealed Bid Auction are considered for reservations and spot plans, respectively, to obtain market-driven prices of the services. The Seasonal Weighted Moving Average method is used to predict the amount of demand in every slot. Finally, the proposed mechanisms are evaluated through simulations and the results confirm the effectiveness of the methods in maximizing the revenue and overall utilization of the available IaaS capacity.

     

Alkaline phosphatase and acid phosphatase levels in saliva and serum of patients with healthy periodontium,gingivitis, and periodontitis before and after scaling with root planing: A clinico-biochemical study

Periodontitis is commonly diagnosed based on clinical parameters. However, the analysis of a few unique biomarkers of the disease process present in the saliva and blood can further assist the estimation of the rate of disease progression.AimThe present study attempted to correlate the alkaline phosphatase (ALP) and acid phosphatase (ACP) levels in saliva and serum between patients with healthy periodontium, gingivitis, and chronic periodontitis.Materials and methodsThe present study was conducted in 135 subjects between 20 and 55 years of age. The subjects were divided into three groups, namely healthy (Group A), gingivitis (Group B), and chronic periodontitis (Group C). The clinical parameters were recorded using the plaque index (PI), gingival index (GI), and probing depth (PD). Saliva and serum were analyzed for ALP and ACP levels using an auto analyzer. All patients underwent scaling and root planning (SRP) along with oral hygiene instructions. Patients were then recalled after four weeks, and blood and saliva samples were collected to estimate ALP and ACP levels prior to clinical examination.ResultsThe clinical parameters exhibited a statistically significant decrease in the PI and GI in both group B and group C after SRP. A significant change in the PD and attachment levels (AL) was observed in the periodontitis group after SRP. The mean salivary & serum ALP levels exhibited a statistically significant decrease in group B & C after SRP. The mean serum ACP levels exhibited a statistically significant decrease in group B & C after SRP However, the salivary ACP levels decrease after SRP was only statistically significant in group C.ConclusionSerum and salivary ALP and ACP levels were markedly decreased in the gingivitis and periodontitis groups after SRP and were positively correlated with the clinical parameters.     

Marine microorganisms as an untapped source of bioactive compounds

The search for novel biologically active molecules has extended to the screening of organisms associated with less explored environments. In this sense, Oceans, which cover nearly the 67% of the globe, are interesting ecosystems characterized by a high biodiversity that is worth being explored. As such, marine microorganisms are highly interesting as promising sources of new bioactive compounds of potential value to humans. Some of these microorganisms are able to survive in extreme marine environments and, as a result, they produce complex molecules with unique biological interesting properties for a wide variety of industrial and biotechnological applications. Thus, different marine microorganisms (fungi, myxomycetes, bacteria, and microalgae) producing compounds with antioxidant, antibacterial, apoptotic, antitumoral and antiviral activities have been already isolated. This review compiles and discusses the discovery of bioactive molecules from marine microorganisms reported from 2018 onwards. Moreover, it highlights the huge potential of marine microorganisms for obtaining highly valuable bioactive compounds.     

Botanical candidates from Saudi Arabian flora as potential therapeutics for Plasmodium infection

Malaria is a lethal parasitic disease affecting over two hundred million people worldwide and kills almost half a million people per year. Until now, there is no curative treatment for this disease that has a substantial morbidity. The available chemotherapeutic agents are unable to completely control the infection with the continuous appearance of drug resistance. Consequently, the search for new therapeutic agents with high safety profiles and low side effects is of paramount importance. Several natural products have been investigated and proven to have antimalarial effects either in vivo or in vitro. A large number of plants have been studied globally for their antimalarial activities. However, studies that have been conducted in this field in Saudi Arabia are not enough. This article presents global and local research on the need for novel natural antimalarial agents with a particular emphasis on studies involving plants from Saudi Arabian flora.