Sequence symmetric modified IGG4 bispecific antibodies

    公开(公告)号:US10221251B2

    公开(公告)日:2019-03-05

    申请号:US14380309

    申请日:2013-02-22

    Abstract: The present disclosure relates to a symmetric bispecific antibody of the class IgG4 comprising two heavy chains which each comprise a variable domain, CH1 domain and a hinge region, wherein in each heavy chain: the cysteine in the CH1 domain which forms an inter-chain disulphide bond with a cysteine in a light chain is substituted with another amino acid; and optionally one or more of the amino acids positioned in the upper hinge region is substituted with cysteine, wherein the constant region sequence of each heavy chain is similar or identical and the variable region in each heavy chain is different, formulations comprising the same, the use of each of the above in treatment and processes for preparing said antibodies and formulations.

    RECOMBINANT BACTERIAL HOST CELL FOR PROTEIN EXPRESSION

    公开(公告)号:US20180258457A1

    公开(公告)日:2018-09-13

    申请号:US15959498

    申请日:2018-04-23

    Abstract: The present disclosure relates to a recombinant gram-negative bacterial cell comprising: a) a mutant spr gene encoding a spr protein having a mutation at one or more amino acids selected from D133, H145, H157, N31, R62, I70, Q73, C94, S95, V98, Q99, R100, L108, Y115, V135, L136, G140, R144 and G147 and b) a gene capable of expressing or overexpressing one or more proteins capable of facilitating protein folding, such as FkpA, Skp, SurA, PPiA and PPiD, wherein the cell has reduced Tsp protein activity compared to a wild-type cell, methods employing the cells, use of the cells in the expression of proteins in particular antibodies, such as anti FcRn antibodies and proteins made by the methods described herein.

    METHOD AND SYSTEM FOR PREDICTING OPTIMAL EPILEPSY TREATMENT REGIMES

    公开(公告)号:US20180211012A1

    公开(公告)日:2018-07-26

    申请号:US15415758

    申请日:2017-01-25

    Abstract: A method of building a machine learning pipeline for predicting the efficacy of anti-epilepsy drug treatment regimens is provided. The method includes providing electronic health records data; constructing a patient cohort from the electronic health records data by selecting patients based on a defined target variable indicating anti-epilepsy drug treatment regimen efficacy; constructing a set features found in or derived from the electronic health records data; electronically processing the patient cohort to identify a subset of the features that are predictive for anti-epilepsy drug treatment regimen efficacy for inclusion in predictive models configured for generating predictions representative of efficacy for a plurality of anti-epilepsy drug treatment regimens; and training the predictive computerized model to generate predictions representative of efficacy for a plurality of anti-epilepsy drug treatment regimens for the patients based on the defined target variable indicating anti-epilepsy drug treatment regimen efficacy.

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