Regulatory status: For research use only, not for use in diagnostic procedures.

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serum, plasma
Species Reactivity
Human, Rat, Mouse, Primate
Intended Use
This highly sensitive "sandwich" test kit is intended for use in the quantitative determination of antibody MMAE conjugate level in serum or plasma of human, mouse, rat, primate, etc. It is useful for pre-clinical and clinical pharmacology study of MMAE Antibody Drug Conjugate (ADC).
Contents of Kit
1. Anti-MMAE Antibody Coated Microplate
2. MMAE Tracer Antibody
3. Tracer Antibody Diluent
4. ELISA Wash Concentrate
5. ELISA HRP Substrate
6. ELISA Stop Solution
7. Assay Buffer
8. Antibody Conjugated Calibrator Zero
Unopened kit: Store at 2-8°C. Do not use the kit beyond the expiration date.
Performance Characteristics
High Dose "hook" effect
This assay has showed that it didn't have any high dose "hook" effect for MMAE ADC levels up to 1,000 µg/mL.
The intra-assay precision was validated by measuring three spiked samples with 16 replicate determinations.
Intra-assay Precision CV%: 2.7%-7.9%
The inter-assay precision was validated by measuring two control levels in duplicate in 8 individual assays.
Inter-assay Precision CV%: 4.8%-5.3%
The sensitivity (lowest limit of detection, LLOD) of this MMAE-ADC ELISA as determined by the corresponding OD value of 2-fold standard deviation above the mean on 8 duplicate determination of zero standard is 0.112 ng/mL. Considering the 1:100 pre-dilution factor of serum or plasma samples, the actual test sensitivity for test sample is about 1.12 pg/ml.
Standard Curve


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A combined logistic and model based approach for fault detection and identification in a climbing robot


Authors: Jiang Yong; Wang Hongguang; Fang Lijin; Zhao Mingyang

This paper presents a combined logistic and model based approach for fault detection and identification (FDI) in the suction foot control of a climbing robot. For this control system, some fault models are easily given by kinematics equations. Moreover, the logic relations of the system states have been known in advance. Based on the combination of the logic reasoning and the model based estimation, the novel approach is properly fit for the FDI application to the climbing robot. First a fault tree (FT) constructed from the target system is used in robot safety analysis by evaluating the basic events (elementary causes) which can lead to a root event (a particular fault). Then, the multiple-model adaptive estimation (MMAE) algorithm is used to reliably detect and identify the model-known faults. Finally, based on the system states of the robot and the results of the MMAE, other faults are detected and identified using the logic reasoning. Experimental results validated that the faults of the sensors and actuators in the suction foot control of the robot can be readily detected and identified by this approach.

Assessing the Interactions of Auristatin Derivatives with Mixed Phospholipid-Sodium Dodecyl Sulfate Aggregate Dispersions


Authors: Ruokonen, Suvi-Katriina; Ekholm, Filip S.; Wiedmer, Susanne K.

The aim of this study was to assess what properties of the pseudostationary phases in electrokinetic capillary chromatography affect the interactions between monomethyl auristatin E (MMAE) and hydrophilically modified structural analogues thereof with various lipophilic phases. MMAE is a widely used cytotoxic agent in antibody-drug conjugates (ADC), which are used as selective biopharmaceutical drugs in the treatment of cancers. MMAE and its derivatives are highly lipophilic, yet they fail to interact with biomimicking phosphatidylcholine-phosphatidylserine liposomes. To reveal what properties affect the interaction of the auristatin derivatives with cell plasma membrane-mimicking vesicles, capillary electrokinetic chromatography was used with four different types of micellar and vesicular pseudostationary phases: pure vesicles, mixed vesicles, mixed micelles, and pure micelles. Vesicular phases were composed of pure phospholipids [dimyristoylphosphatidylcholine (DMPC) and dilauroylphosphatidylcholine (DLPC)] and phospholipid-surfactant mixtures [sodium dodecyl sulfate, (SDS) with DMPC and DLPC] while the micellar phases comprised pure surfactant (SDS) and surfactant-phospholipid mixtures (SDS-DMPC and SDS-DLPC). In addition, differential scanning calorimetry and dynamic light scattering were used to monitor the aggregate composition. Our data shows that the interaction between hydrophobic auristatin derivatives and hydrophobic pseudostationary phases critically depends on the type, size, and hydrogen bonding capability of the pseudostationary phases.

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