diagonal-method-to-measure-synergy-among-any-number-of-drugs

Diagonal Method to Measure Synergy Among Any Number of Drugs

A synergistic drug combination has a higher efficacy compared to the effects of individual drugs. Checkerboard assays, where drugs are combined in many ...

Sabanci University

Harvard Medical School

&#x836F;&#x7269;&#x534F;&#x540C;&#x4F5C;&#x7528;&#x5141;&#x8BB8;&#x8981;&#x5B9E;&#x73B0;&#x7EC4;&#x4EF6;&#x836F;&#x7269;&#x4F4E;&#x5242;&#x91CF;&#x7684;&#x6CBB;&#x7597;&#x4F5C;&#x7528;&#x3002;&#x6BD2;&#x54C1;&#x76EE;&#x6807;&#x884C;&#x4E8B;&#x5E76;&#x884C;&#x8DEF;&#x5F84; (&#39;&#x5177;&#x4F53;&#x534F;&#x540C;&#39;) &#x7684;&#x57FA;&#x56E0;&#x7684;&#x4EA7;&#x54C1;&#x65F6;&#xFF0C;&#x5C31;&#x4F1A;&#x5BFC;&#x81F4;&#x836F;&#x7269;&#x7684;&#x534F;&#x540C;&#x4F5C;&#x7528;&#x3002;&#x8FD9;&#x79CD;&#x60C5;&#x51B5;&#x4E0B;&#x7684;&#x836F;&#x7269;&#x534F;&#x540C;&#x4F5C;&#x7528;&#x5E94;&#x503E;&#x5411;&#x4E8E;&#x5BF9;&#x5E94;&#x4E8E;&#x76F8;&#x5E94;&#x7684;&#x9776;&#x57FA;&#x56E0;&#x7684;&#x9057;&#x4F20;&#x534F;&#x540C;&#x76F8;&#x4E92;&#x4F5C;&#x7528;&#x3002;&#x6216;&#x8005;&#xFF0C;&#x5F53;&#x4E00;&#x79CD;&#x836F;&#x7269;&#x975E;&#x5177;&#x4F53;&#x5F71;&#x54CD;&#x7684;&#x8BB8;&#x591A;&#x5176;&#x4ED6;&#x836F;&#x7269;&#xFF0C;&#x4F8B;&#x5982;&#xFF0C;&#x901A;&#x8FC7;&#x589E;&#x52A0;&#x589E;&#x52A0;&#x751F;&#x7269;&#x5229;&#x7528;&#x5EA6;&#xFF0C;&#x53EF;&#x80FD;&#x4F1A;&#x51FA;&#x73B0; &#39;&#x6DF7;&#x6742;&#x7684;&#x534F;&#x540C;&#x4F5C;&#x7528;&#x3002;&#x4E3A;&#x4E86;&#x8BC4;&#x4F30;&#x8FD9;&#x4E9B;&#x836F;&#x7269;&#x534F;&#x540C;&#x4F5C;&#x7528;&#x7C7B;&#x578B;&#x76F8;&#x5BF9;&#x4E30;&#x5BCC;&#xFF0C;&#x6211;&#x4EEC;&#x5BA1;&#x67E5;&#x4E86; 200 &#x53CC;&#x6297;&#x771F;&#x83CC;&#x836F;&#x7269;&#x5728;&#x917F;&#x9152;&#x9175;&#x6BCD;&#x4E2D;&#x3002;&#x6211;&#x4EEC;&#x53D1;&#x73B0; 38 &#x6297;&#x771F;&#x83CC;&#x7684;&#x534F;&#x540C;&#x589E;&#x6548;&#x4F5C;&#x7528;&#xFF0C;&#x5176;&#x4E2D; 37 &#x540D;&#x5C0F;&#x8BF4;&#x3002;&#x867D;&#x7136;&#x836F;&#x7269;&#x534F;&#x540C;&#x4F5C;&#x7528;&#x7684; 14 &#x4F8B;&#x7B26;&#x5408;&#x9057;&#x4F20;&#x76F8;&#x4E92;&#x4F5C;&#x7528;&#xFF0C;92%&#x7684;&#x534F;&#x540C;&#x589E;&#x6548;&#x4F5C;&#x7528;&#xFF0C;&#x6211;&#x4EEC;&#x53D1;&#x73B0;&#x53C2;&#x4E0E;&#x53EA;&#x6709;&#x516D;&#x4E2A;&#x7ECF;&#x5E38;&#x534F;&#x540C;&#x836F;&#x7269;&#x3002;&#x867D;&#x7136;&#x6EE5;&#x4EA4;&#x7684;&#x56DB;&#x79CD;&#x836F;&#x7269;&#x53EF;&#x4EE5;&#x89E3;&#x91CA;&#x751F;&#x7269;&#x5229;&#x7528;&#x5EA6;&#x6A21;&#x5F0F;&#x4E0B;&#xFF0C;&#x662F;&#x5B8C;&#x5168;&#x51FA;&#x4E4E;&#x610F;&#x6599;&#x7684;&#x4ED6;&#x514B;&#x83AB;&#x53F8;&#x548C;&#x8112;&#x6EE5;&#x4EA4;&#x3002;&#x867D;&#x7136;&#x8BB8;&#x591A;&#x836F;&#x7269;&#x534F;&#x540C;&#x4F5C;&#x7528;&#x5BF9;&#x5E94;&#x7684;&#x9057;&#x4F20;&#x4EA4;&#x4E92;&#x4F5C;&#x7528;&#xFF0C;&#x5927;&#x591A;&#x6570;&#x836F;&#x7269;&#x534F;&#x540C;&#x4F5C;&#x7528;&#x4F3C;&#x4E4E;&#x4ECE;&#x975E;&#x7279;&#x5B9A;&#x6DF7;&#x6742;&#x7684;&#x534F;&#x540C;&#x4F5C;&#x7528;&#x7684;&#x7ED3;&#x679C;&#x3002;

&#x6709;&#x7CFB;&#x7EDF;&#x5730;&#x63A2;&#x8BA8;&#x534F;&#x540C;&#x836F;&#x5BF9;&#x3002;

Drug synergy allows a therapeutic effect to be achieved with lower doses of component drugs. Drug synergy can result when drugs target the products of genes that act in parallel pathways ('specific synergy'). Such cases of drug synergy should tend to correspond to synergistic genetic interaction between the corresponding target genes. Alternatively, 'promiscuous synergy' can arise when one drug non-specifically increases the effects of many other drugs, for example, by increased bioavailability. To assess the relative abundance of these drug synergy types, we examined 200 pairs of antifungal drugs in S. cerevisiae. We found 38 antifungal synergies, 37 of which were novel. While 14 cases of drug synergy corresponded to genetic interaction, 92% of the synergies we discovered involved only six frequently synergistic drugs. Although promiscuity of four drugs can be explained under the bioavailability model, the promiscuity of Tacrolimus and Pentamidine was completely unexpected. While many drug synergies correspond to genetic interactions, the majority of drug synergies appear to result from non-specific promiscuous synergy.

Systematic exploration of synergistic drug pairs.

Synergie des m&#xE9;dicaments permet un effet th&#xE9;rapeutique &#xE0; atteindre avec des doses plus faibles de m&#xE9;dicaments qui le composent. Synergie de drogues peut entra&#xEE;ner lorsque les m&#xE9;dicaments cibler les produits de g&#xE8;nes qui agissent dans les voies parall&#xE8;les (&#xAB;synergie sp&#xE9;cifique&#xBB;). Ces cas de synergie de drogues devrait avoir tendance &#xE0; correspondre &#xE0; une interaction synergique entre les g&#xE9;n&#xE9;tique des g&#xE8;nes cibles correspondants. Alternativement, la &#xAB;synergie promiscuit&#xE9;&#xBB; peuvent survenir lorsque un m&#xE9;dicament non-sp&#xE9;cifiquement augmente les effets de nombreux autres m&#xE9;dicaments, par exemple, par une biodisponibilit&#xE9; accrue. Afin d&#x26;#39;&#xE9;valuer l&#x26;#39;abondance relative de ces types de synergie des m&#xE9;dicaments, nous avons examin&#xE9; 200 paires de m&#xE9;dicaments antifongiques chez S. cerevisiae. Nous avons trouv&#xE9; 38 des synergies antifongiques, dont 37 &#xE9;taient nouvelles. Alors que 14 cas de synergie de drogues correspondait &#xE0; l&#x26;#39;interaction g&#xE9;n&#xE9;tique, 92% des synergies que nous avons d&#xE9;couverts impliqu&#xE9;s seulement six m&#xE9;dicaments fr&#xE9;quemment synergiques. Bien que la promiscuit&#xE9; de quatre m&#xE9;dicaments peut s&#x26;#39;expliquer dans le cadre du mod&#xE8;le de biodisponibilit&#xE9;, la promiscuit&#xE9; de Tacrolimus et la pentamidine &#xE9;tait totalement inattendu. Alors que les synergies de nombreux m&#xE9;dicaments correspondent &#xE0; des interactions g&#xE9;n&#xE9;tiques, la majorit&#xE9; des synergies de drogues semblent d&#xE9;couler du non-sp&#xE9;cifique synergie promiscuit&#xE9;.

Exploration syst&#xE9;matique de paires de drogues synergiques

Droge Synergie erm&ouml;glicht eine therapeutische Wirkung mit niedrigeren Dosen Komponente Drogen erreicht werden. Droge Synergie kann entstehen, wenn Medikamente die Produkte von Genen konzentrieren, die in parallelen Bahnen (&#39;spezifische Synergie&#39;) zu handeln. Solchen F&auml;llen der Droge Synergie sollte tendenziell synergistische genetische Interaktion zwischen den entsprechenden Zielgene entsprechen. Alternativ kann &#39;unterschiedslosen Synergie&#39; entstehen, wenn eine Droge unspezifisch die Auswirkungen vieler anderer Drogen, z. B. erh&ouml;hte Bioverf&uuml;gbarkeit erh&ouml;ht. Um die relative H&auml;ufigkeit dieser Droge Synergie-Typen zu bewerten, haben wir 200 Paare von Antimykotika in S. Cerevisiae gepr&uuml;ft. Wir fanden 38 antimykotische Synergien, die 37 davon Roman waren. W&auml;hrend 14 F&auml;llen Droge Synergie genetische Interaktion entsprach, nachgingen 92 % die Synergien entdeckten wir nur sechs h&auml;ufig synergistische Drogen. Obwohl Promiskuit&auml;t von vier Medikamenten unter die Bioverf&uuml;gbarkeit-Modell erkl&auml;rt werden kann, kam die Promiskuit&auml;t Tacrolimus und Pentamidin v&ouml;llig unerwartet. W&auml;hrend viele Drogen Synergien genetische Interaktionen entsprechen, scheinen die meisten Drogen Synergien aus unspezifischen unterschiedslosen Synergien ergeben.

Systematische Erforschung der synergistischen Droge-Paare.

Droga sinergia permette un effetto terapeutico essere raggiunto con basse dosi di farmaci componente. Sinergia di droga pu&ograve; comportare quando farmaci target i prodotti dei geni che agiscono nelle vie parallele (&#39;specifica sinergia&#39;). Tali casi di sinergia farmaco dovrebbero tendenzialmente corrispondono alla sinergica interazione genetica tra i geni bersaglio corrispondente. In alternativa, &#39;promiscua sinergia&#39; pu&ograve; verificarsi quando un farmaco non specifico aumenta gli effetti di molti farmaci, ad esempio, di maggiore biodisponibilit&agrave;. Per valutare l&#39;abbondanza relativa di questi tipi di sinergia di droga, abbiamo esaminato 200 paia di farmaci antimicotici in S. cerevisiae. Abbiamo trovato 38 sinergie antifungine, 37 dei quali erano romanzo. Mentre 14 casi di sinergia farmaco corrispondevano all&#39;interazione genetica, 92% delle sinergie che abbiamo scoperto coinvolto solo sei farmaci spesso sinergici. Sebbene la promiscuit&agrave; dei quattro farmaci pu&ograve; essere spiegato con il modello di biodisponibilit&agrave;, la promiscuit&agrave; di Tacrolimus e pentamidina era completamente inaspettata. Mentre molte sinergie droga corrispondono alle interazioni genetiche, la maggior parte delle sinergie droga sembra derivare dalla sinergia promiscua non specifici.

Esplorazione sistematica delle coppie farmaco sinergico.

&#x85AC;&#x306E;&#x76F8;&#x4E57;&#x52B9;&#x679C;&#x306F;&#x3001;&#x6CBB;&#x7642;&#x52B9;&#x679C;&#x306F;&#x3001;&#x30B3;&#x30F3;&#x30DD;&#x30FC;&#x30CD;&#x30F3;&#x30C8;&#x306E;&#x85AC;&#x306E;&#x4F4E;&#x7528;&#x91CF;&#x3067;&#x9054;&#x6210;&#x3059;&#x308B;&#x3053;&#x3068;&#x304C;&#x3067;&#x304D;&#x307E;&#x3059;&#x3002;&#x85AC;&#x306F;&#xFF08; &#x26;#39;&#x7279;&#x5B9A;&#x306E;&#x76F8;&#x4E57;&#x52B9;&#x679C;&#x26;#39;&#xFF09;&#x4E26;&#x5217;&#x7D4C;&#x8DEF;&#x306B;&#x4F5C;&#x7528;&#x3059;&#x308B;&#x907A;&#x4F1D;&#x5B50;&#x7523;&#x7269;&#x3092;&#x6A19;&#x7684;&#x3068;&#x3059;&#x308B;&#x3068;&#x304D;&#x85AC;&#x5264;&#x306E;&#x76F8;&#x4E57;&#x52B9;&#x679C;&#x304C;&#x751F;&#x3058;&#x308B;&#x53EF;&#x80FD;&#x6027;&#x304C;&#x3042;&#x308A;&#x307E;&#x3059;&#x3002;&#x85AC;&#x306E;&#x76F8;&#x4E57;&#x52B9;&#x679C;&#x306E;&#x3088;&#x3046;&#x306A;&#x5834;&#x5408;&#x306F;&#x3001;&#x5BFE;&#x5FDC;&#x3059;&#x308B;&#x6A19;&#x7684;&#x907A;&#x4F1D;&#x5B50;&#x306E;&#x76F8;&#x4E57;&#x907A;&#x4F1D;&#x7684;&#x76F8;&#x4E92;&#x4F5C;&#x7528;&#x306B;&#x5BFE;&#x5FDC;&#x3059;&#x308B;&#x50BE;&#x5411;&#x304C;&#x3042;&#x308B;&#x3079;&#x304D;&#x3067;&#x3042;&#x308B;&#x3002; 1&#x85AC;&#x306F;&#x975E;&#x7279;&#x7570;&#x7684;&#x306B;&#x5897;&#x52A0;&#x3057;&#x305F;&#x751F;&#x7269;&#x5B66;&#x7684;&#x5229;&#x7528;&#x80FD;&#x306F;&#x3001;&#x4F8B;&#x3048;&#x3070;&#x3001;&#x4ED6;&#x306E;&#x591A;&#x304F;&#x306E;&#x85AC;&#x5264;&#x306E;&#x52B9;&#x679C;&#x3092;&#x5897;&#x52A0;&#x3055;&#x305B;&#x305F;&#x3068;&#x304D;&#x306B;&#x4EE3;&#x308F;&#x308A;&#x306B;&#x3001; &#x26;quot;&#x7121;&#x5DEE;&#x5225;&#x306E;&#x76F8;&#x4E57;&#x52B9;&#x679C;&#x26;quot;&#x306F;&#x767A;&#x751F;&#x3059;&#x308B;&#x53EF;&#x80FD;&#x6027;&#x304C;&#x3042;&#x308A;&#x307E;&#x3059;&#x3002;&#x3053;&#x308C;&#x3089;&#x306E;&#x85AC;&#x5264;&#x306E;&#x76F8;&#x4E57;&#x52B9;&#x679C;&#x306E;&#x7A2E;&#x985E;&#x306E;&#x76F8;&#x5BFE;&#x5B58;&#x5728;&#x91CF;&#x3092;&#x8A55;&#x4FA1;&#x3059;&#x308B;&#x305F;&#x3081;&#x306B;&#x3001;&#x6211;&#x3005;&#x306F;&#x3001;S.&#x30BB;&#x30EC;&#x30D3;&#x30B7;&#x30A8;&#x306E;&#x6297;&#x771F;&#x83CC;&#x85AC;&#x306E;200&#x30DA;&#x30A2;&#x3092;&#x691C;&#x8A0E;&#x3057;&#x305F;&#x3002;&#x6211;&#x3005;&#x306F;&#x5C0F;&#x8AAC;&#x3067;&#x3042;&#x3063;&#x305F;37&#x3001;&#x305D;&#x306E;&#x3046;&#x3061;38&#x6297;&#x771F;&#x83CC;&#x5264;&#x306E;&#x76F8;&#x4E57;&#x52B9;&#x679C;&#x3092;&#x767A;&#x898B;&#x3057;&#x305F;&#x3002;&#x85AC;&#x306E;&#x76F8;&#x4E57;&#x52B9;&#x679C;&#x306E;14&#x4F8B;&#x306F;&#x3001;&#x907A;&#x4F1D;&#x200B;&#x200B;&#x7684;&#x76F8;&#x4E92;&#x4F5C;&#x7528;&#x306B;&#x5BFE;&#x5FDC;&#x3057;&#x306A;&#x304C;&#x3089;&#x3001;&#x6211;&#x3005;&#x304C;&#x767A;&#x898B;&#x3055;&#x308C;&#x305F;&#x76F8;&#x4E57;&#x52B9;&#x679C;&#x306E;92&#xFF05;&#x306F;&#x308F;&#x305A;&#x304B;6&#x983B;&#x7E41;&#x306B;&#x76F8;&#x4E57;&#x7684;&#x306A;&#x85AC;&#x7269;&#x3092;&#x542B;&#x3093;&#x3060;&#x3002; 4&#x533B;&#x85AC;&#x54C1;&#x306E;&#x4E71;&#x4EA4;&#x304C;&#x30D0;&#x30A4;&#x30AA;&#x30A2;&#x30D9;&#x30A4;&#x30E9;&#x30D3;&#x30EA;&#x30C6;&#x30A3;&#x30E2;&#x30C7;&#x30EB;&#x306B;&#x57FA;&#x3065;&#x3044;&#x3066;&#x8AAC;&#x660E;&#x3059;&#x308B;&#x3053;&#x3068;&#x304C;&#x3067;&#x304D;&#x307E;&#x3059;&#x304C;&#x3001;&#x30BF;&#x30AF;&#x30ED;&#x30EA;&#x30E0;&#x30B9;&#x3068;&#x30DA;&#x30F3;&#x30BF;&#x30DF;&#x30B8;&#x30F3;&#x306E;&#x4E71;&#x4EA4;&#x306F;&#x5B8C;&#x5168;&#x306B;&#x4E88;&#x60F3;&#x5916;&#x3067;&#x3042;&#x3063;&#x305F;&#x3002;&#x591A;&#x304F;&#x306E;&#x85AC;&#x5264;&#x306E;&#x76F8;&#x4E57;&#x52B9;&#x679C;&#x306F;&#x3001;&#x907A;&#x4F1D;&#x5B50;&#x306E;&#x76F8;&#x4E92;&#x4F5C;&#x7528;&#x306B;&#x5BFE;&#x5FDC;&#x3057;&#x306A;&#x304C;&#x3089;&#x3001;&#x85AC;&#x306E;&#x76F8;&#x4E57;&#x52B9;&#x679C;&#x306E;&#x5927;&#x534A;&#x306F;&#x3001;&#x975E;&#x7279;&#x5B9A;&#x306E;&#x7121;&#x5DEE;&#x5225;&#x76F8;&#x4E57;&#x52B9;&#x679C;&#x306B;&#x8D77;&#x56E0;&#x3059;&#x308B;&#x3068;&#x8868;&#x793A;&#x3055;&#x308C;&#x307E;&#x3059;&#x3002;

&#x76F8;&#x4E57;&#x85AC;&#x7269;&#x30DA;&#x30A2;&#x306E;&#x7CFB;&#x7D71;&#x7684;&#x63A2;&#x67FB;

&#xB9C8;&#xC57D; &#xC2DC;&#xB108;&#xC9C0; &#xD6A8;&#xACFC; &#xCE58;&#xB8CC; &#xD6A8;&#xACFC;&#xB97C; &#xAD6C;&#xC131; &#xC57D;&#xBB3C;&#xC758; &#xB354; &#xB0AE;&#xC740; &#xBCF5;&#xC6A9;&#xB7C9;&#xC744; &#xAD6C;&#xD604;&#xD560; &#xC218; &#xC788;&#xC2B5;&#xB2C8;&#xB2E4;. &#xB9C8;&#xC57D; &#xC2DC;&#xB108;&#xC9C0; &#xC758;&#xC57D;&#xD488; &#xB300;&#xC0C1;&#xC73C;&#xB85C; &#xBCD1;&#xB82C; &#xACBD;&#xB85C; (&#xC774; &#xD558; &#39; &#xD2B9;&#xC815; &#xC2DC;&#xB108;&#xC9C0;&#39;)&#xC5D0;&#xC11C; &#xB3D9;&#xC791; &#xD558;&#xB294; &#xC720;&#xC804;&#xC790;&#xC758; &#xC81C;&#xD488; &#xACBD;&#xC6B0; &#xBC1C;&#xC0DD;&#xD560; &#xC218; &#xC788;&#xC2B5;&#xB2C8;&#xB2E4;. &#xB9C8;&#xC57D; &#xC2DC;&#xB108;&#xC9C0;&#xC758; &#xACBD;&#xC6B0; &#xD574;&#xC57C; &#xD574;&#xB2F9; &#xB300;&#xC0C1; &#xC720;&#xC804;&#xC790; &#xAC04;&#xC758; &#xC2DC;&#xB108;&#xC9C0; &#xC720;&#xC804;&#xC790; &#xC0C1;&#xD638; &#xC791;&#xC6A9;&#xC5D0; &#xD574;&#xB2F9; &#xD558;&#xB294; &#xACBD;&#xD5A5;&#xC774; &#xC788;&#xC2B5;&#xB2C8;&#xB2E4;. &#xB610;&#xB294;, &#39;&#xBB34;&#xCC28;&#xBCC4; &#xC2DC;&#xB108;&#xC9C0;&#39; &#xB9C8;&#xC57D; bioavailability &#xC99D;&#xAC00; &#xD558; &#xC5EC; &#xC608;&#xB97C; &#xB4E4;&#xC5B4;, &#xC99D;&#xAC00; &#xB9CE;&#xC740; &#xB2E4;&#xB978; &#xC57D;&#xBB3C;&#xC758; &#xD6A8;&#xACFC;-&#xD2B9;&#xD788; &#xB54C; &#xBC1C;&#xC0DD;&#xD560; &#xC218; &#xC788;&#xC2B5;&#xB2C8;&#xB2E4;. &#xC774;&#xB7EC;&#xD55C; &#xB9C8;&#xC57D; &#xC2DC;&#xB108;&#xC9C0; &#xC885;&#xB958;&#xC758; &#xC0C1;&#xB300; &#xD48D;&#xBD80;&#xD55C; &#xD3C9;&#xAC00;, &#xC6B0;&#xB9AC;&#xB294; S. cerevisiae&#xC5D0; &#xD56D;&#xC9C4;&#xADE0; &#xC57D;&#xBB3C;&#xC758; 200 &#xC30D; &#xAC80;&#xC0AC;. &#xC6B0;&#xB9AC;&#xB294; 37 &#xD588;&#xB2E4; &#xC18C;&#xC124; 38 &#xD56D;&#xC9C4;&#xADE0; &#xC2DC;&#xB108;&#xC9C0; &#xD6A8;&#xACFC; &#xBC1C;&#xACAC;. &#xB9C8;&#xC57D; &#xC2DC;&#xB108;&#xC9C0;&#xC758; 14 &#xACBD;&#xC6B0; &#xC720;&#xC804;&#xC790;&#xC758; &#xC0C1;&#xD638; &#xC791;&#xC6A9;&#xC5D0; &#xB300;&#xC751;, &#xD558;&#xB294; &#xB3D9;&#xC548; &#xC6B0;&#xB9AC;&#xAC00; &#xBC1C;&#xACAC; &#xD558;&#xB294; &#xC2DC;&#xB108;&#xC9C0; &#xD6A8;&#xACFC;&#xC758; 92 %6 &#xC790;&#xC8FC; &#xC2DC;&#xB108;&#xC9C0; &#xB9C8;&#xC57D; &#xAD00;&#xB828;. 4 &#xAC1C;&#xC758; &#xC57D;&#xBB3C;&#xC758; &#xC131;&#xD589;&#xC704; bioavailability &#xBAA8;&#xB378;&#xC5D0;&#xC11C; &#xC124;&#xBA85; &#xB420; &#xC218; &#xC788;&#xB2E4;, &#xBE44;&#xB85D; Tacrolimus &#xBC0F; Pentamidine&#xC758; &#xC131;&#xD589;&#xC704; &#xC644;&#xC804;&#xD788; &#xC608;&#xC0C1; &#xB418;&#xC9C0; &#xC54A;&#xC558;&#xC2B5;&#xB2C8;&#xB2E4;. &#xB3D9;&#xC548; &#xC720;&#xC804;&#xC790;&#xC758; &#xC0C1;&#xD638; &#xC791;&#xC6A9;&#xC5D0; &#xD574;&#xB2F9; &#xD558;&#xB294; &#xB9CE;&#xC740; &#xC57D;&#xBB3C; &#xC2DC;&#xB108;&#xC9C0; &#xD6A8;&#xACFC;, &#xB9C8;&#xC57D; &#xC2DC;&#xB108;&#xC9C0; &#xD6A8;&#xACFC;&#xC758; &#xB300;&#xBD80;&#xBD84; &#xC77C;&#xBC18;&#xC801;&#xC778; &#xBB34;&#xCC28;&#xBCC4; &#xC2DC;&#xB108;&#xC9C0;&#xC5D0;&#xC11C; &#xBC1C;&#xC0DD;&#xD560; &#xB4EF;&#xD558;&#xB2E4;.

&#xC2DC;&#xB108;&#xC9C0; &#xB9C8;&#xC57D; &#xC30D;&#xC758; &#xCCB4;&#xACC4;&#xC801;&#xC778; &#xD0D0;&#xC0AC;&#xC785;&#xB2C8;&#xB2E4;.

Sinergia de Drogas permite un efecto terap&#xE9;utico que se consigue con dosis m&#xE1;s bajas de las drogas de los componentes. Sinergia de drogas puede resultar cuando medicamentos se dirigen a los productos de los genes que act&#xFA;an en las v&#xED;as paralelas (&#x26;quot;sinergia espec&#xED;fico&#xBB;). Estos casos de sinergia de drogas debe tienden a corresponder a la interacci&#xF3;n sin&#xE9;rgica entre la gen&#xE9;tica de los genes diana correspondientes. Alternativamente, &#x26;quot;sinergia promiscuo &#x26;#39;puede surgir cuando un f&#xE1;rmaco no aumenta espec&#xED;ficamente los efectos de muchos otros f&#xE1;rmacos, por ejemplo, por aumento de la biodisponibilidad. Para evaluar la abundancia relativa de estos tipos de drogas de sinergia, se examinaron 200 pares de f&#xE1;rmacos antimic&#xF3;ticos en S. cerevisiae. Hemos encontrado 38 sinergias antif&#xFA;ngicos, de los cuales 37 eran nuevas. Mientras que 14 casos de la sinergia de drogas corresponde a la interacci&#xF3;n gen&#xE9;tica, el 92% de las sinergias que hemos descubierto f&#xE1;rmacos implicados con frecuencia sin&#xE9;rgicos s&#xF3;lo seis. A pesar de la promiscuidad de los cuatro tipos de medicamentos se puede explicar bajo el modelo de la biodisponibilidad, la promiscuidad de Tacrolimus y pentamidina fue completamente inesperado. Mientras que muchas sinergias de drogas corresponden a las interacciones gen&#xE9;ticas, la mayor&#xED;a de las sinergias de drogas parecen ser el resultado de la no-espec&#xED;fica sinergia promiscuo.

Exploraci&#xF3;n sistem&#xE1;tica de f&#xE1;rmacos en par sin&#xE9;rgicos

One-third of breast cancers display amplifications of the ERBB2 gene encoding the HER2 kinase receptor. Trastuzumab, a humanized antibody directed against an epitope on subdomain IV of the extracellular domain of HER2 is used for therapy of HER2-overexpressing mammary tumors. However, many tumors are either natively resistant or acquire resistance against Trastuzumab. Antibodies directed to different epitopes on the extracellular domain of HER2 are promising candidates for replacement or combinatorial therapy. For example, Pertuzumab that binds to subdomain II of HER2 extracellular domain and inhibits receptor dimerization is under clinical trial. Alternative antibodies directed to novel HER2 epitopes may serve as additional tools for breast cancer therapy. Our aim was to generate novel anti-HER2 monoclonal antibodies inhibiting the growth of breast cancer cells, either alone or in combination with tumor necrosis factor-α (TNF-α).

Novel anti-HER2 monoclonal antibodies: synergy and antagonism with tumor necrosis factor-α.

Small molecules with selective efficacy can be used as drugs. Drugs typically have a therapeutic dose range where the benefit from intended effects outweighs the cost of unintended (side) effects. Herein, I use case scenarios to illustrate potential advantages and pitfalls of treatment with drugs alone or in combination. Combinations of drugs may show surprising effects given the effects of individual drugs, in phenomena known as drug interactions. Drug interactions can be classified as synergistic or antagonistic, if the effect of the combination is higher or lower than expected, respectively. A better understanding of drug interactions and their relationship to phenotypes offers hope in finding drug combinations that have high therapeutic values.

Drugs and their interactions.

Green algae offer sustainable, clean and eco-friendly energy resource. However, production efficiency needs to be improved. Increasing cellular lipid levels by nitrogen depletion is one of the most studied strategies. Despite this, the underlying physiological and biochemical mechanisms of this response have not been well defined. Algae species adapted to hypersaline conditions can be cultivated in salty waters which are not useful for agriculture or consumption. Due to their inherent extreme cultivation conditions, use of hypersaline algae species is better suited for avoiding culture contamination issues. In this study, we identified a new halophilic Dunaliella salina strain by using 18S ribosomal RNA gene sequencing. We found that growth and biomass productivities of this strain were directly related to nitrogen levels, as the highest biomass concentration under 0.05 mM or 5 mM nitrogen regimes were 495 mg/l and 1409 mg/l, respectively. We also confirmed that nitrogen limitation increased cellular lipid content up to 35% under 0.05 mM nitrogen concentration. In order to gain insight into the mechanisms of this phenomenon, we applied fluorometric, flow cytometric and spectrophotometric methods to measure oxidative stress and enzymatic defence mechanisms. Under nitrogen depleted cultivation conditions, we observed increased lipid peroxidation by measuring an important oxidative stress marker, malondialdehyde and enhanced activation of catalase, ascorbate peroxidase and superoxide dismutase antioxidant enzymes. These observations indicated that oxidative stress is accompanied by increased lipid content in the green alga. In addition, we also showed that at optimum cultivation conditions, inducing oxidative stress by application of exogenous H2O2 leads to increased cellular lipid content up to 44% when compared with non-treated control groups. Our results support that oxidative stress and lipid overproduction are linked. Importantly, these results also suggest that oxidative stress mediates lipid accumulation. Understanding such relationships may provide guidance for efficient production of algal biodiesels. 

Oxidative stress is a mediator for increased lipid accumulation in a newly isolated Dunaliella salina strain.

One drug may suppress the effects of another. Although knowledge of drug suppression is vital to avoid efficacy-reducing drug interactions or discover countermeasures for chemical toxins, drug-drug suppression relationships have not been systematically mapped. Here, we analyze the growth response of Saccharomyces cerevisiae to anti-fungal compound ("drug") pairs. Among 440 ordered drug pairs, we identified 94 suppressive drug interactions. Using only pairs not selected on the basis of their suppression behavior, we provide an estimate of the prevalence of suppressive interactions between anti-fungal compounds as 17%. Analysis of the drug suppression network suggested that Bromopyruvate is a frequently suppressive drug and Staurosporine is a frequently suppressed drug. We investigated potential explanations for suppressive drug interactions, including chemogenomic analysis, coaggregation, and pH effects, allowing us to explain the interaction tendencies of Bromopyruvate. 

Large-scale identification and analysis of suppressive drug interactions.

Chemogenomic experiments, where genetic and chemical perturbations are combined, provide data for discovering the relationships between genotype and phenotype. Traditionally, analysis of chemogenomic datasets has been done considering the sensitivity of the deletion strains to chemicals, and this has shed light on drug mechanism of action and detecting drug targets. Here, we computationally analyzed a large chemogenomic dataset, which combines more than 300 chemicals with virtually all gene deletion strains in the yeast S. cerevisiae. In addition to sensitivity relation between deletion strains and chemicals, we also considered the deletion strains that are resistant to chemicals. We found a small set of genes whose deletion makes the cell resistant to many chemicals. Curiously, these genes were enriched for functions related to RNA metabolism. Our approach allowed us to generate a network of drugs and genes that are connected with resistance or sensitivity relationships. As a quality assessment, we showed that the higher order motifs found in this network are consistent with biological expectations. Finally, we constructed a biologically relevant network projection pertaining to drug similarities, and analyzed this network projection in detail. We propose this drug similarity network as a useful tool for understanding drug mechanism of action. 

A drug similarity network for understanding drug mechanism of action.

Revealing the genetic changes responsible for antibiotic resistance can be critical for developing novel antibiotic therapies. However, systematic studies correlating genotype to phenotype in the context of antibiotic resistance have been missing. In order to fill in this gap, we evolved 88 isogenic Escherichia coli populations against 22 antibiotics for 3 weeks. For every drug, two populations were evolved under strong selection and two populations were evolved under mild selection. By quantifying evolved populations' resistances against all 22 drugs, we constructed two separate cross-resistance networks for strongly and mildly selected populations. Subsequently, we sequenced representative colonies isolated from evolved populations for revealing the genetic basis for novel phenotypes. Bacterial populations that evolved resistance against antibiotics under strong selection acquired high levels of cross-resistance against several antibiotics, whereas other bacterial populations evolved under milder selection acquired relatively weaker cross-resistance. In addition, we found that strongly selected strains against aminoglycosides became more susceptible to five other drug classes compared with their wild-type ancestor as a result of a point mutation on TrkH, an ion transporter protein. Our findings suggest that selection strength is an important parameter contributing to the complexity of antibiotic resistance problem and use of high doses of antibiotics to clear infections has the potential to promote increase of cross-resistance in clinics. 

Strength of selection pressure is an important parameter contributing to the complexity of antibiotic resistance evolution.

Physicochemical properties of compounds have been instrumental in selecting lead compounds with increased drug-likeness. However, the relationship between physicochemical properties of constituent drugs and the tendency to exhibit drug interaction has not been systematically studied. We assembled physicochemical descriptors for a set of antifungal compounds ("drugs") previously examined for interaction. Analyzing the relationship between molecular weight, lipophilicity, H-bond donor, and H-bond acceptor values for drugs and their propensity to show pairwise antifungal drug synergy, we found that combinations of two lipophilic drugs had a greater tendency to show drug synergy. We developed a more refined decision tree model that successfully predicted drug synergy in stringent cross-validation tests based on only lipophilicity of drugs. Our predictions achieved a precision of 63% and allowed successful prediction for 58% of synergistic drug pairs, suggesting that this phenomenon can extend our understanding for a substantial fraction of synergistic drug interactions. We also generated and analyzed a large-scale synergistic human toxicity network, in which we observed that combinations of lipophilic compounds show a tendency for increased toxicity. Thus, lipophilicity, a simple and easily determined molecular descriptor, is a powerful predictor of drug synergy. It is well established that lipophilic compounds (i) are promiscuous, having many targets in the cell, and (ii) often penetrate into the cell via the cellular membrane by passive diffusion. We discuss the positive relationship between drug lipophilicity and drug synergy in the context of potential drug synergy mechanisms. 

Target-independent prediction of drug synergies using only drug lipophilicity.

The development of treatments involving combinations of drugs is a promising approach towards combating complex or multifactorial disorders. However, the large number of compound combinations that can be generated, even from small compound collections, means that exhaustive experimental testing is infeasible. The ability to predict the behaviour of compound combinations in biological systems, whittling down the number of combinations to be tested, is therefore crucial. Here, we review the current state-of-the-art in the field of compound combination modelling, with the aim to support the development of approaches that, as we hope, will finally lead to an integration of chemical with systems-level biological information for predicting the effect of chemical mixtures. 

Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives.

Identifying genetic alterations that prime a cancer cell to respond to a particular therapeutic agent can facilitate the development of precision cancer medicines. Cancer cell-line (CCL) profiling of small-molecule sensitivity has emerged as an unbiased method to assess the relationships between genetic or cellular features of CCLs and small-molecule response. Here, we developed annotated cluster multidimensional enrichment analysis to explore the associations between groups of small molecules and groups of CCLs in a new, quantitative sensitivity dataset. This analysis reveals insights into small-molecule mechanisms of action, and genomic features that associate with CCL response to small-molecule treatment. We are able to recapitulate known relationships between FDA-approved therapies and cancer dependencies and to uncover new relationships, including for KRAS-mutant cancers and neuroblastoma. To enable the cancer community to explore these data, and to generate novel hypotheses, we created an updated version of the Cancer Therapeutic Response Portal (CTRP v2).

Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset.

Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical-genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug-interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less-studied pathogens by leveraging chemogenomics data in model organisms.

Chemogenomics and orthology-based design of antibiotic combination therapies.

Mycobacteria grow and divide asymmetrically, creating variability in growth pole age, growth properties, and antibiotic susceptibilities. Here, we investigate the importance of growth pole age and other growth properties in determining the spectrum of responses of Mycobacterium smegmatis to challenge with rifampicin. We used a combination of live-cell microscopy and modeling to prospectively identify subpopulations with altered rifampicin susceptibility. We found two subpopulations that had increased susceptibility. At the initiation of treatment, susceptible cells were either small and at early stages of the cell cycle, or large and in later stages of their cell cycle. In contrast to this temporal window of susceptibility, tolerance was associated with factors inherited at division: long birth length and mature growth poles. Thus, rifampicin response is complex and due to a combination of differences established from both asymmetric division and the timing of treatment relative to cell birth.

Temporal and intrinsic factors of rifampicin tolerance in mycobacteria.

Development of drug resistance limits the efficacy of targeted therapies. Alternative approaches using different combinations of therapeutic agents to inhibit several pathways could be a more effective strategy for treating cancer. The effects of the approved epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (gefitinib) or a multi-targeted kinase inhibitor (sorafenib) in combination with a histone deacetylase inhibitor (vorinostat) on cell proliferation, cell cycle distribution, apoptosis, and signaling pathway activation in human lung adenocarcinoma and hepatocarcinoma cells with wild-type  and mutant  were investigated. The effects of the synergistic drug combinations were also studied in human lung adenocarcinoma and hepatocarcinoma cells in vivo. The combination of gefitinib and vorinostat synergistically reduced cell growth and strongly induced apoptosis through inhibition of the insulin-like growth factor-1 receptor/protein kinase B (IGF-1R/AKT)-dependent signaling pathway. Moreover, the gefitinib and vorinostat combination strongly inhibited tumor growth in mice with lung adenocarcinoma or hepatocarcinoma tumor xenografts. In contrast, the combination of sorafenib and vorinostat did not inhibit cell proliferation compared to a single treatment and induced G/M cell cycle arrest without apoptosis. The sorafenib and vorinostat combination sustained the IGF-1R-, AKT-, and mitogen-activated protein kinase-dependent signaling pathways. These results showed that there was synergistic cytotoxicity when vorinostat was combined with gefitinib for both lung adenocarcinoma and hepatocarcinoma with mutant  in vitro and in vivo but that the combination of vorinostat with sorafenib did not show any benefit. These findings highlight the important role of the IGF-1R/AKT pathway in the resistance to targeted therapies and support the use of histone deacetylase inhibitors in combination with EGFR-tyrosine kinase inhibitors, especially for treating patients with mutant  resistant to other treatments.

Synergistic activity of vorinostat combined with gefitinib but not with sorafenib in mutant KRAS human non-small cell lung cancers and hepatocarcinoma.

Combination antibiotic therapies are clinically important in the fight against bacterial infections. However, the search space of drug combinations is large, making the identification of effective combinations a challenging task. Here, we present a computational framework that uses substructure profiles derived from the molecular structures of drugs and predicts antibiotic interactions. Using a previously published data set of 153 drug pairs, we showed that substructure profiles are useful in predicting synergy. We experimentally measured the interaction of 123 new drug pairs, as a prospective validation set for our approach, and identified 37 new synergistic pairs. Of the 12 pairs predicted to be synergistic, 10 were experimentally validated, corresponding to a 2.8-fold enrichment. Having thus validated our methodology, we produced a compendium of interaction predictions for all pairwise combinations among 100 antibiotics. Our methodology can make reliable antibiotic interaction predictions for any antibiotic pair within the applicability domain of the model since it solely requires chemical structures as an input.

Prediction of Antibiotic Interactions Using Descriptors Derived from Molecular Structure.

Small interfering RNAs (siRNA) are short nucleic acid fragments of about 20-27 nucleotides, which can inhibit the expression of specific genes. siRNA based RNAi technology has emerged as a promising method for the treatment of a variety of diseases. However, a major limitation in the therapeutic use of siRNA is its rapid degradation in plasma and cellular cytoplasm, resulting in short half-life. In addition, as siRNA molecules cannot penetrate into the cell efficiently, it is required to use a carrier system for its delivery. In this work, chemically and morphologically different calcium phosphate (CaP) nanoparticles, including spherical-like hydroxyapatite (HA-s), needle-like hydroxyapatite (HA-n) and calcium deficient hydroxyapatite (CDHA) nanoparticles were synthesized by the sol-gel technique and the effects of particle characteristics on the binding capacity of siRNA were investigated. In order to enhance the gene loading efficiency, the nanoparticles were functionalized with arginine and the morphological and their structural characteristics were analyzed. The addition of arginine did not significantly change the particle sizes; however, it provided a significantly increased binding of siRNA for all types of CaP nanoparticles, as revealed by spectrophotometric measurements analysis. Arginine functionalized HA-n nanoparticles showed the best binding behavior with siRNA among the other nanoparticles due to its high, positive zeta potential (+18.8mV) and high surface area of Ca rich "c" plane. MTT cytotoxicity assays demonstrated that all the nanoparticles tested herein were biocompatible. Our results suggest that high siRNA entrapment in each of the three modified non-toxic CaP nanoparticles make them promising candidates as a non-viral vector for delivering therapeutic siRNA molecules to treat cancer.

Synthesis and characterization of amino acid-functionalized calcium phosphate nanoparticles for siRNA delivery.

Combinations of three or more drugs are used to treat many diseases, including tuberculosis. Thus, it is important to understand how synergistic or antagonistic drug interactions affect the efficacy of combination therapies. However, our understanding of high-order drug interactions is limited because of the lack of both efficient measurement methods and theoretical framework for analysis and interpretation. We developed an efficient experimental sampling and scoring method [diagonal measurement of -way drug interactions (DiaMOND)] to measure drug interactions for combinations of any number of drugs. DiaMOND provides an efficient alternative to checkerboard assays, which are commonly used to measure drug interactions. We established a geometric framework to factorize high-order drug interactions into lower-order components, thereby establishing a road map of how to use lower-order measurements to predict high-order interactions. Our framework is a generalized Loewe additivity model for high-order drug interactions. Using DiaMOND, we identified and analyzed synergistic and antagonistic antibiotic combinations against . Efficient measurement and factorization of high-order drug interactions by DiaMOND are broadly applicable to other cell types and disease models.

Article	Year
Systematic exploration of synergistic drug pairs. Molecular systems biology\| PubMed ID: 22068327	2011
Novel anti-HER2 monoclonal antibodies: synergy and antagonism with tumor necrosis factor-α. BMC cancer\| PubMed ID: 23033967	2012
Drugs and their interactions. Current drug discovery technologies\| PubMed ID: 23237673	2013
Oxidative stress is a mediator for increased lipid accumulation in a newly isolated Dunaliella salina strain. PloS one\| PubMed ID: 24651514	2014
Large-scale identification and analysis of suppressive drug interactions. Chemistry & biology\| PubMed ID: 24704506	2014
A drug similarity network for understanding drug mechanism of action. Journal of bioinformatics and computational biology\| PubMed ID: 24712534	2014
Strength of selection pressure is an important parameter contributing to the complexity of antibiotic resistance evolution. Molecular biology and evolution\| PubMed ID: 24962091	2014
Target-independent prediction of drug synergies using only drug lipophilicity. Journal of chemical information and modeling\| PubMed ID: 25026390	2014
Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives. Drug discovery today\| PubMed ID: 26360051	2016
Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset. Cancer discovery\| PubMed ID: 26482930	2015
Chemogenomics and orthology-based design of antibiotic combination therapies. Molecular systems biology\| PubMed ID: 27222539	2016
Temporal and intrinsic factors of rifampicin tolerance in mycobacteria. Proceedings of the National Academy of Sciences of the United States of America\| PubMed ID: 27357669	2016
Synergistic activity of vorinostat combined with gefitinib but not with sorafenib in mutant KRAS human non-small cell lung cancers and hepatocarcinoma. OncoTargets and therapy\| PubMed ID: 27877053	2016
Prediction of Antibiotic Interactions Using Descriptors Derived from Molecular Structure. Journal of medicinal chemistry\| PubMed ID: 28383902	2017
Synthesis and characterization of amino acid-functionalized calcium phosphate nanoparticles for siRNA delivery. Colloids and surfaces. B, Biointerfaces\| PubMed ID: 28689100	2017
Efficient measurement and factorization of high-order drug interactions in . Science advances\| PubMed ID: 29026882	2017

Murat Cokol

Biography

JoVE Journal Publications

Other Publications